Gabriele Martelloni

ORCID: 0000-0003-2699-6013
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About
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Research Areas
  • COVID-19 epidemiological studies
  • Quantum many-body systems
  • SARS-CoV-2 and COVID-19 Research
  • Opinion Dynamics and Social Influence
  • Advanced Thermodynamics and Statistical Mechanics
  • Quantum Chromodynamics and Particle Interactions
  • Complex Systems and Time Series Analysis
  • Machine Learning in Bioinformatics
  • Black Holes and Theoretical Physics
  • Physics of Superconductivity and Magnetism
  • Statistical Mechanics and Entropy
  • Advanced Mathematical Physics Problems
  • Cold Atom Physics and Bose-Einstein Condensates
  • PARP inhibition in cancer therapy
  • Noncommutative and Quantum Gravity Theories
  • COVID-19 Clinical Research Studies

University of Siena
2024

Azienda Ospedaliera Universitaria Senese
2023

National Interuniversity Consortium of Materials Science and Technology
2020

University of Florence
2013-2020

Scuola Internazionale Superiore di Studi Avanzati
2016

Istituto Nazionale di Fisica Nucleare, Sezione di Pisa
2014-2015

University of Pisa
2014-2015

Istituto Nazionale di Fisica Nucleare, Sezione di Firenze
2013

We employ the Quench Action Method (QAM) for a recently considered geometrical quantum quench: two free fermionic chains initially at different temperatures are joined together in middle and let evolve unitarily with translation invariant Hamiltonian. show that stationary regimes reached long times, depending on interplay between observation time scale T total length L of system. emergence non-equilibrium steady state (NESS) supporting an energy current much smaller than system size L. then...

10.1103/physreva.91.021603 article EN Physical Review A 2015-02-24

We consider a non-interacting Fermi gas in d dimensions, both the nonrelativistic and relativistic case.The system of size L is initially prepared into two halves R, each them thermalized at different temperatures, T R respectively.At time t = 0 are put contact entire left to evolve unitarily.We show that, thermodynamic limit, evolution particle energy densities perfectly described by semiclassical approach which permits analytically evaluate correspondent stationary currents.In particular,...

10.1088/1742-5468/2014/08/p08006 article EN Journal of Statistical Mechanics Theory and Experiment 2014-08-05

Ground states ofinteracting QFTs are non-Gaussian states, i.e. their connected n-point correlation functions do not vanish for , in contrast to the free QFT case. We show that, when ground state of an interacting evolves under a massive long time (a scenario that can be realised by quantum quench), decay and all local physical observables equilibrate values given Gaussian density matrix retains memory only two-point initial function. The argument hinges upon fundamental principle cluster...

10.1088/1751-8113/49/9/095002 article EN Journal of Physics A Mathematical and Theoretical 2016-01-25

Systems with long-range interactions display a short-time relaxation towards quasistationary states (QSSs) whose lifetime increases the system size. In paradigmatic Hamiltonian mean-field model (HMF) out-of-equilibrium phase transitions are predicted and numerically detected which separate homogeneous (zero magnetization) inhomogeneous (nonzero QSSs. former regime, velocity distribution presents (at least) two large, symmetric bumps, cannot be self-consistently explained by resorting to...

10.1103/physreve.93.022107 article EN Physical review. E 2016-02-04

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, Fallerini et al. (Human genetics, 2022, 141, 147–173), were used to define an interpretable machine learning model for predicting severity. First, converted into sets Boolean features, depending on the absence or presence each gene. An ensemble LASSO logistic regression models was identify most informative features with respect genetic bases After that, selected by these models, combined...

10.3389/fgene.2024.1362469 article EN cc-by Frontiers in Genetics 2024-05-22

ABSTRACT The impact of common and rare variants in COVID-19 host genetics is widely studied [16]. Here, were used to define an interpretable machine learning model for predicting severity. Firstly, converted into sets Boolean features, depending on the absence or presence each gene. An ensemble LASSO logistic regression models was identify most informative features with respect genetic bases After that, selected by these models, combined Integrated PolyGenic Score, so called IPGS, which...

10.1101/2023.02.06.527291 preprint EN bioRxiv (Cold Spring Harbor Laboratory) 2023-02-07
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