The emotional contagion in children with autism spectrum disorder

Studies of the last decade have demonstrated that children with Autism Spectrum Disorder (ASD) showed difficulties in language, social and relational areas, but they had also impairment in the mechanisms of embodied simulation, namely the imitative behaviors that allow the body to give an experiential meaning to own and other’s emotions. The identification of this specific emotional response in ASD children, also defined as emotional contagion, allows to move the therapeutic focus from reducing the behavioral symptomatic expressions of the child to promoting the expression of his ability of emotional regulation. The aim of this study was to investigate the presence of emotional contagion in 53 ASD children aged between 22 and 66 months, through the Test of emotional contagion and verify the presence of compromised emotional contagion areas. Our findings have shown that the severity of the disorder is closely related to the inability of the child to respond to the emotional stimuli, regardless from cognitive abilities, and that emotion to which children responded most frequently was happiness, while the one who responded less was anger

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

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

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

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