Massive photons and Lorentz violation

All quadratic translation- and gauge-invariant photon operators for Lorentz breakdown are included into the Stueckelberg Lagrangian for massive photons in a generalized \xi-gauge. The corresponding dispersion relation and tree-level propagator are determined exactly, and some leading-order results are derived. The question of how to include such Lorentz-violating effects into a perturbative quantum-field expansion is addressed. Applications of these results within Lorentz-breaking quantum field theories include the regularization of infrared divergences as well as the free propagation of massive vector bosons.Comment: 12 pages, 1 figur

Testing Lorentz symmetry with atoms and Light

This article reports on the Fifth Meeting on CPT and Lorentz Symmetry, CPT'10, held at the end of June 2010 in Bloomington, Indiana, USA. The focus is on recent tests of Lorentz symmetry using atomic and optical physics.Comment: 10 pages; invited conference report for CAMOP section of Physica Script

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

A generative adversarial network (GAN) technique for internet of medical things data

The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning

Sensitivity of Logic Learning Machine for reliability in safety-critical systems

Nowadays Artificial Intelligence (AI) is bursting in many fields, including critical ones, giving rise to reliable AI, that means ensuring safety of autonomous decisions. As the false negatives may have a safety impact (e.g., in a mobility scenario, prediction of no collision, but collision in reality), the aim is to push false negatives as close to zero as possible, thus designing ‘`safety regions’' in the feature space with statistical zero error. We show here how sensitivity analysis of an eXplainable AI model drives such statistical assurance. We test and compare the proposed algorithms on two different datasets (physical fatigue and vehicle platooning) and achieve quite different conclusions in terms of results that strongly depend on the level of noise in the dataset rather than on the algorithms at hand

A particle counting immunoassay for the direct detection of Clostridium difficile serogroup specific antigen in faecal specimens

The potential of a particle counting immunoassay (PACIA) for the direct detection of Clostridium difficile serogroup G specific antigen in faecal specimens was evaluated. F(ab')2 fragments from a rabbit anti-serogroup G antiserum were covalently coupled to carboxylated latex beads. This reagent was mixed with acid extracts of faecal specimens and the reaction was assayed with an optical counter which discriminated unagglutinated from agglutinated latex particles. Culture for C. difficile, faecal cytotoxin detection, PACIA and serogrouping of C. difficile isolates were performed on 249 stools. Of the 71 culture-negative specimens, none gave a positive result in the cytotoxin assay or in PACIA. Faecal cytotoxin was detected in 100 of the 178 culture-positive specimens. PACIA was positive for 63 of the 71 faecal specimens that yielded serogroup G C. difficile on culture. PACIA gave negative results for all other culture-positive stools tested with one exception, from which a serogroup A7 C. difficile strain was isolated. PACIA detection of serogroup G antigen in faecal specimens showed a sensitivity of 88.7%, a specificity of 99.7%, a predictive value of a positive culture with a serogroup G strain of 98.4%, and a predictive value for specimens that were culture-negative for a serogroup G strain of 95.6%. The results indicate that PACIA with specific antiserum is a rapid and reliable method for detecting serogroup specific antigens of C. difficile in faecal specimens. Clinical applications of the method are discussed