Neutrino oscillations and Lorentz Invariance Violation in a Finslerian Geometrical model

Neutrino oscillations are one of the first evidences of physics beyond the Standard Model (SM). Since Lorentz Invariance is a fundamental symmetry of the SM, recently also neutrino physics has been explored to verify the eventual modification of this symmetry and its potential magnitude. In this work we study the consequences of the introduction of Lorentz Invariance Violation (LIV) in the high energy neutrinos propagation and evaluate the impact of this eventual violation on the oscillation predictions. An effective theory explaining these physical effects is introduced via Modified Dispersion Relations. This approach, originally introduced by Coleman and Glashow, corresponds in our model to a modification of the special relativity geometry. Moreover, the generalization of this perspective leads to the introduction of a maximum attainable velocity which is specific of the particle. This can be formalized in Finsler geometry, a more general theory of space-time. In the present paper the impact of this kind of LIV on neutrino phenomenology is studied, in particular by analyzing the corrections introduced in neutrino oscillation probabilities for different values of neutrino energies and baselines of experimental interest. The possibility of further improving the present constraints on CPT-even LIV coefficients by means of our analysis is also discussed.Comment: Accepted for publication with minor revisions, will appear on European Physics Journal

Polimi at CLinkaRT: a Conditional Random Field vs a BERT-based approach

In the context of the EVALITA 2023 challenge, we present the models we have developed for the CLinkaRT task, which aims to identify medical examinations and their corresponding results in Italian clinical documents. We propose two distinct approaches: one utilising a Conditional Random Field (CRF), a probabilistic graphical model traditionally used for Named Entity Recognition, and the other based on BERT, the transformer-based model that is currently state-of-the-art for many Natural Language Processing tasks. Both models incorporate external knowledge from publicly available medical resources and are enhanced with heuristic rules to establish associations between exams and results. Our comparative analysis elects the CRF-based model as the winner, securing the third position in the competition ranking, but the BERT-based model demonstrated competitive performance

Use of winemaking by-products as an ingredient for tomato puree : the effect of particle size on product quality

Formulations of tomato puree with grape skin fibres (Chardonnay variety) having varying particle sizes were studied. The contents of flavonoids (by HPLC\u2013DAD) and proanthocyanidins (n-butanol/HCl assay), reducing capacity (ferric ion reducing antioxidant power, FRAP) and anti-glycation activity by a bovine serum albumin (BSA)/fructose model system were analysed in vitro. A liking test was performed with consumers. Stabilization was carried out by either an intensive autoclave treatment or an optimised microwave-treatment achieving 6D-reduction of the target microorganism (Alicylobacillus acidoterrestris). In the fortified tomato purees, the solubility of proanthocyanidins decreased, but was partly restored by autoclave treatment, which also caused deglycosylation of flavonol glycosides. Microwave treatment did not show any effect on phenolics. The reducing capacity and ability to inhibit protein glycation greatly increased in the fortified purees. The particle sizes of solids in the formulations played a major role with respect to the consumers\u2019 liking, with the smallest fraction showing maximum ratings

Clustering Italian medical texts: a case study on referrals

In the medical domain, there is a large amount of valuable information that is stored in textual format. These unstructured data have long been ignored, due to the difficulties of introducing them in statistical models, but in the last years, the field of Natural Language Processing (NLP) has seen relevant improvements, with models capable of achieving relevant results in various tasks, including information extraction, classification and clustering. NLP models are typically language-specific and often domain-specific, but most of the work to date has been focused on the English language, especially in the medical domain. In this work, we propose a pipeline for clustering Italian medical texts, with a case study on clinical questions reported in referral

Hilbert Series for Flavor Invariants of the Standard Model

The Hilbert series is computed for the lepton flavor invariants of the Standard Model with three generations including the right-handed neutrino sector needed to generate light neutrino masses via the see-saw mechanism. We also compute the Hilbert series of the quark flavor invariants for the case of four generations.Comment: 6 page