Pressure induced magnetic phase separation in La0.75_{0.75}Ca0.25_{0.25}MnO3_{3} manganite

The pressure dependence of the Curie temperature T$_{C}(P)$ in La$_{0.75}$Ca$_{0.25}$MnO$_{3}$ was determined by neutron diffraction up to 8 GPa, and compared with the metallization temperature T$_{IM}(P)$ \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$_2$, $f_{q}(T)$, 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 $q$-behavior of $f_{q}(T)$ 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