5,259 research outputs found
Pressure induced magnetic phase separation in LaCaMnO manganite
The pressure dependence of the Curie temperature T in
LaCaMnO was determined by neutron diffraction up to 8
GPa, and compared with the metallization temperature T \cite{irprl}.
The behavior of the two temperatures appears similar over the whole pressure
range suggesting a key role of magnetic double exchange also in the pressure
regime where the superexchange interaction is dominant. Coexistence of
antiferromagnetic and ferromagnetic peaks at high pressure and low temperature
indicates a phase separated regime which is well reproduced with a dynamical
mean-field calculation for a simplified model. A new P-T phase diagram has been
proposed on the basis of the whole set of experimental data.Comment: 5 pages, 4 figure
From the emotional integration to the cognitive construction: the developmental approach of Turtle Project in children with autism spectrum disorder
Background: Children with autism spectrum disorder show a deficit in neurobiological processes. This deficit
hinders the development of intentional behavior and appropriate problem-solving, leading the child to implement
repetitive and stereotyped behaviors and to have difficulties in reciprocal interactions, empathy and in the
development of a theory of mind. The objective of this research is to verify the effectiveness of a relationship-based
approach on the positive evolution of autistic symptoms.
Method: A sample of 80 children with autism spectrum disorder was monitored during the first four years of
therapy, through a clinical diagnostic assessment at the time of intake and then in two follow-up.
Results: The results showed that through the Autism Diagnostic Observation Schedule it is possible to
assess the socio-relational key elements on which the therapy is based. There was evidence, in fact, of significant
improvements after two and four years of therapy, both for children with severe autistic symptoms and for those in
autistic spectrum.
Conclusions: Socio-relational aspects represent the primary element on which work in therapy with autistic
children and can be considered as indicators of a positive evolution and prognosis that will produce improvements
even in the cognitive are
Deep Learning for Galaxy Mergers in the Galaxy Main Sequence
Starburst galaxies are often found to be the result of galaxy mergers. As a
result, galaxy mergers are often believed to lie above the galaxy main
sequence: the tight correlation between stellar mass and star formation rate.
Here, we aim to test this claim. Deep learning techniques are applied to images
from the Sloan Digital Sky Survey to provide visual-like classifications for
over 340 000 objects between redshifts of 0.005 and 0.1. The aim of this
classification is to split the galaxy population into merger and non-merger
systems and we are currently achieving an accuracy of 91.5%. Stellar masses and
star formation rates are also estimated using panchromatic data for the entire
galaxy population. With these preliminary data, the mergers are placed onto the
full galaxy main sequence, where we find that merging systems lie across the
entire star formation rate - stellar mass plane.Comment: 4 pages, 1 figure. For Proceedings IAU Symposium No. 34
Identifying Galaxy Mergers in Observations and Simulations with Deep Learning
Mergers are an important aspect of galaxy formation and evolution. We aim to
test whether deep learning techniques can be used to reproduce visual
classification of observations, physical classification of simulations and
highlight any differences between these two classifications. With one of the
main difficulties of merger studies being the lack of a truth sample, we can
use our method to test biases in visually identified merger catalogues. A
convolutional neural network architecture was developed and trained in two
ways: one with observations from SDSS and one with simulated galaxies from
EAGLE, processed to mimic the SDSS observations. The SDSS images were also
classified by the simulation trained network and the EAGLE images classified by
the observation trained network. The observationally trained network achieves
an accuracy of 91.5% while the simulation trained network achieves 65.2% on the
visually classified SDSS and physically classified EAGLE images respectively.
Classifying the SDSS images with the simulation trained network was less
successful, only achieving an accuracy of 64.6%, while classifying the EAGLE
images with the observation network was very poor, achieving an accuracy of
only 53.0% with preferential assignment to the non-merger classification. This
suggests that most of the simulated mergers do not have conspicuous merger
features and visually identified merger catalogues from observations are
incomplete and biased towards certain merger types. The networks trained and
tested with the same data perform the best, with observations performing better
than simulations, a result of the observational sample being biased towards
conspicuous mergers. Classifying SDSS observations with the simulation trained
network has proven to work, providing tantalizing prospects for using
simulation trained networks for galaxy identification in large surveys.Comment: Submitted to A&A, revised after first referee report. 20 pages, 22
figures, 14 tables, 1 appendi
Pathway to a Compact SASE FEL Device
Newly developed high peak power lasers have opened the possibilities of
driving coherent light sources operating with laser plasma accelerated beams
and wave undulators. We speculate on the combination of these two concepts and
show that the merging of the underlying technologies could lead to new and
interesting possibilities to achieve truly compact, coherent radiator devices
Estimating the generation interval from the incidence rate, the optimal quarantine duration and the efficiency of fast switching periodic protocols for COVID‑19
The transmissibility of an infectious disease is usually quantified in terms of the reproduction
number Rt representing, at a given time, the average number of secondary cases caused by an
infected individual. Recent studies have enlightened the central role played by w(z), the distribution
of generation times z, namely the time between successive infections in a transmission chain. In
standard approaches this quantity is usually substituted by the distribution of serial intervals, which
is obtained by contact tracing after measuring the time between onset of symptoms in successive
cases. Unfortunately, this substitution can cause important biases in the estimate of Rt . Here we
present a novel method which allows us to simultaneously obtain the optimal functional form of
w(z) together with the daily evolution of Rt , over the course of an epidemic. The method uses, as
unique information, the daily series of incidence rate and thus overcomes biases present in standard
approaches. We apply our method to one year of data from COVID-19 officially reported cases in the
21 Italian regions, since the first confirmed case on February 2020. We find that w(z) has mean value
z ≃ 6 days with a standard deviation a ≃ 1 day, for all Italian regions, and these values are stable
even if one considers only the first 10 days of data recording. This indicates that an estimate of the
most relevant transmission parameters can be already available in the early stage of a pandemic. We
use this information to obtain the optimal quarantine duration and to demonstrate that, in the case
of COVID-19, post-lockdown mitigation policies, such as fast periodic switching and/or alternating
quarantine, can be very efficient
Deep Saturated Free Electron Laser Oscillators and Frozen Spikes
We analyze the behavior of Free Electron Laser (FEL) oscillators operating in
the deep saturated regime and point out the formation of sub-peaks of the
optical pulse. They are very stable configurations, having a width
corresponding to a coherence length. We speculate on the physical mechanisms
underlying their growth and attempt an identification with FEL mode locked
structures associated with Super Modes. Their impact on the intra-cavity
nonlinear harmonic generation is also discussed along with the possibility of
exploiting them as cavity out-coupler.Comment: 28 page
Ergodicity breaking in strong and network-forming glassy system
The temperature dependence of the non-ergodicity factor of vitreous GeO,
, as deduced from elastic and quasi-elastic neutron scattering
experiments, is analyzed. The data are collected in a wide range of
temperatures from the glassy phase, up to the glass transition temperature, and
well above into the undercooled liquid state. Notwithstanding the investigated
system is classified as prototype of strong glass, it is found that the
temperature- and the -behavior of follow some of the predictions
of Mode Coupling Theory. The experimental data support the hypothesis of the
existence of an ergodic to non-ergodic transition occurring also in network
forming glassy systems
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