starMC: an automata based CTL* model checker

Model-checking of temporal logic formulae is a widely used technique for the verification of systems. CTL [Image: see text] is a temporal logic that allows to consider an intermix of both branching behaviours (like in CTL) and linear behaviours (LTL), overcoming the limitations of LTL (that cannot express “possibility”) and CTL (cannot fully express fairness). Nevertheless CTL [Image: see text] model-checkers are uncommon. This paper presents (1) the algorithms for a fully symbolic automata-based approach for CTL [Image: see text] , and (2) their implementation in the open-source tool starMC, a CTL [Image: see text] model checker for systems specified as Petri nets. Testing has been conducted on thousands of formulas over almost a hundred models. The experiments show that the fully symbolic automata-based approach of starMC can compute the set of states that satisfy a CTL [Image: see text] formula for very large models (non trivial formulas for state spaces larger than 10(480) states are evaluated in less than a minute)

The impact of inflation on heterogeneous groups of households: an application to Italy

This paper explores the determinants of the heterogeneity in the expenditure behaviours of the Italian households, using the Households Expenditure Survey provided by the Italian National Institute of Statistics (ISTAT) for the year 2005. We assume that differences among consumers are associated with differences in their economic and socio-demographic characteristics (such as gender, employment status and age of the householder, number of household components, presence of under 18 years old components), and we look for those characteristics that better differentiate groups of households according to their purchasing patterns. We apply a nonparametric discriminant analysis based on the various expenditure budget components, and detect the most discrìminating partitions of families. The technique allows us also to identify the specific goods of consumption that significantly differ across the groups identified by the best partitions. We then study the different effects of the price dynamics on subgroups of households, and propose consumer price indices specific for the optimal households groups

Streamlining models with explanations in the learning loop

Several explainable AI methods allow a Machine Learning user to get insights on the classification process of a black-box model in the form of local linear explanations. With such information, the user can judge which features are locally relevant for the classification outcome, and get an understanding of how the model reasons. Standard supervised learning processes are purely driven by the original features and target labels, without any feedback loop informed by the local relevance of the features identified by the post-hoc explanations. In this paper, we exploit this newly obtained information to design a feature engineering phase, where we combine explanations with feature values. To do so, we develop two different strategies, named Iterative Dataset Weighting and Targeted Replacement Values, which generate streamlined models that better mimic the explanation process presented to the user. We show how these streamlined models compare to the original black-box classifiers, in terms of accuracy and compactness of the newly produced explanations.Comment: 16 pages, 10 figures, available repositor

Hereditary spastic paraplegia and axonal motor neuropathy caused by a novel SPG3A de novo mutation

Abstract Mutations in the SPG3A gene (atlastin protein) cause approximately 10% of autosomal-dominant hereditary spastic paraplegia. Most patients with an SPG3A mutation present with a pure phenotype and early-onset disease, although complicated forms with peripheral neuropathy are also reported. We report a new heterozygous S398F mutation in exon 12 of the SPG3A gene causing a very early-onset spastic paraplegia in association with motor axonal neuropathy in a 4-year-old girl resembling diplegic cerebral palsy

Head and neck radiotherapy amid the COVID‑19 pandemic: practice recommendations of the Italian Association of Radiotherapy and Clinical Oncology (AIRO)

Abstract Management of patients with head and neck cancers (HNCs) is challenging for the Radiation Oncologist, especially in the COVID-19 era. The Italian Society of Radiotherapy and Clinical Oncology (AIRO) identified the need of practice recommendations on logistic issues, treatment delivery and healthcare personnel’s protection in a time of limited resources. A panel of 15 national experts on HNCs completed a modified Delphi process. A five-point Likert scale was used; the chosen cut-offs for strong agreement and agreement were 75% and 66%, respectively. Items were organized into two sections: (1) general recommendations (10 items) and (2) special recommendations (45 items), detailing a set of procedures to be applied to all specific phases of the Radiation Oncology workflow. The distribution of facilities across the country was as follows: 47% Northern, 33% Central and 20% Southern regions. There was agreement or strong agreement across the majority (93%) of proposed items including treatment strategies, use of personal protection devices, set-up modifications and follow-up re-scheduling. Guaranteeing treatment delivery for HNC patients is well-recognized in Radiation Oncology. Our recommendations provide a flexible tool for management both in the pandemic and post-pandemic phase of the COVID-19 outbreak

LINEE GUIDA NAZIONALI PER LE AUTOPSIE A SCOPO FORENSE IN MEDICINA VETERINARIA

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LINEE GUIDA NAZIONALI PER LE AUTOPSIE A SCOPO FORENSE IN MEDICINA VETERINARIA

Vedi Prefazione nel testo