Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Telecommunications in the ICT Age: From Research to Applications

The human society in the information age deeply relies on digital information processing, communication and storage. Photonic routing and switchingis expected to be exploited in future all-optical networks. Channel coding is needed in order to protect information against natural disturbances, and modern coding schemes are able to reach the ultimate limits predicted by Shannon. On the other hand, postquantum cryptography is necessary for assuring security against cyber attackers, possibly provided with quantum computers. Source coding, especially in video data compression, is recommended for optimizing the bandwidth usage. Spread spectrum systems can solve the problem of radio transmissions over common frequency bands. These technologies are of crucial importance for the evolution of networks and of the whole Internet, allowing people to interact each other and access information in the web. Nowadays, the conventional Internet of people has moved into the pervasive Internet of Things providing innovative services in a variety of application fields. In this respect, domotic systems, based on ambient and wearable sensors, appear of dramatic importance in the design of future assisted living protocols

Premorbid academic and social functioning in patients with schizophrenia and its associations with negative symptoms and cognition

Objective: The study aimed to explore premorbid academic and social functioning in patients with schizophrenia, and its associations with the severity of negative symptoms and neurocognitive impairment. Method: Premorbid adjustment (PA) in patients with schizophrenia was compared to early adjustment in unaffected first-degree relatives and healthy controls. Its associations with psychopathology, cognition, and real-life functioning were investigated. The associations of PA with primary negative symptoms and their two factors were explored. Results: We found an impairment of academic and social PA in patients (P ≤ 0.000001) and an impairment of academic aspects of early adjustment in relatives (P ≤ 0.01). Patients with poor PA showed greater severity of negative symptoms (limited to avolition after excluding the effect of depression/parkinsonism), working memory, social cognition, and real-life functioning (P ≤ 0.01 to ≤0.000001). Worse academic and social PA were associated with greater severity of psychopathology, cognitive impairment, and real-life functioning impairment (P ≤ 0.000001). Regression analyses showed that worse PA in the academic domain was mainly associated to the impairment of working memory, whereas worse PA in the social domain to avolition (P ≤ 0.000001). Conclusion: Our findings suggest that poor early adjustment may represent a marker of vulnerability to schizophrenia and highlight the need for preventive/early interventions based on psychosocial and/or cognitive programs

Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder

Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients

Unpublished Mediterranean records of marine alien and cryptogenic species

Good datasets of geo-referenced records of alien species are a prerequisite for assessing the spatio-temporal dynamics of biological invasions, their invasive potential, and the magnitude of their impacts. However, with the exception of first records on a country level or wider regions, observations of species presence tend to remain unpublished, buried in scattered repositories or in the personal databases of experts. Through an initiative to collect, harmonize and make such unpublished data for marine alien and cryptogenic species in the Mediterranean Sea available, a large dataset comprising 5376 records was created. It includes records of 239 alien or cryptogenic taxa (192 Animalia, 24 Plantae, 23 Chromista) from 19 countries surrounding the Mediterranean Sea. In terms of records, the most reported Phyla in descending order were Chordata, Mollusca, Chlorophyta, Arthropoda, and Rhodophyta. The most recorded species was Caulerpa cylindracea, followed by Siganus luridus, Magallana sp. (cf. gigas or angulata) and Pterois miles. The dataset includes records from 1972 to 2020, with the highest number of records observed in 2018. Among the records of the dataset, Dictyota acutiloba is a first record for the Mediterranean Sea. Nine first country records are also included: the alga Caulerpa taxifolia var. distichophylla, the cube boxfish Ostracion cubicus, and the cleaner shrimp Urocaridella pulchella from Israel; the sponge Paraleucilla magna from Libya and Slovenia; the lumpfish Cyclopterus lumpus from Cyprus; the bryozoan Celleporaria vermiformis and the polychaetes Prionospio depauperata and Notomastus aberans from Malta.JRC.D.2-Water and Marine Resource