Giulio Tani Raffaelli

ORCID: 0000-0003-0866-5210
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About
Contact & Profiles
Research Areas
  • Advanced Neuroimaging Techniques and Applications
  • Misinformation and Its Impacts
  • Functional Brain Connectivity Studies
  • EEG and Brain-Computer Interfaces
  • MRI in cancer diagnosis
  • Semantic Web and Ontologies
  • NMR spectroscopy and applications
  • Neural dynamics and brain function
  • Names, Identity, and Discrimination Research
  • Authorship Attribution and Profiling

Czech Academy of Sciences, Institute of Computer Science
2024

Sapienza University of Rome
2022

Neural tissue is a hierarchical multiscale system with intracellular and extracellular diffusion compartments at different length scales. The normal of bulk water in tissues not able to detect the specific features complex system, providing nonlocal, measurement averaged on 10-20 μm scale. Being probe sub-micrometric quantify new local parameters, transient anomalous (tAD) would dramatically increase diagnostic potential MRI (DMRI) detecting collective sub-micro architectural changes human...

10.3389/fnins.2021.797642 article EN cc-by Frontiers in Neuroscience 2022-02-15

Since the first studies in functional connectivity, Pearson’s correlation has been primary tool to determine relatedness between activity of different brain locations. Over years, concern over information neglected by pushed toward using measures accounting for non-linearity. However, some suggest that, at typical observation scale, a linear description captures vast majority information. Therefore, we measured fraction that would be lost and which regions affected most. We considered fMRI,...

10.1101/2024.11.17.623635 preprint EN cc-by bioRxiv (Cold Spring Harbor Laboratory) 2024-11-18

Urn models for innovation have proven to capture fundamental empirical laws shared by several real-world processes. The so-called urn model with triggering includes, as particular cases, an representation of the two-parameter Poisson-Dirichlet process and Dirichlet process, seminal in Bayesian non-parametric inference. In this work, we leverage connection introduce a novel approach quantifying closeness between symbolic sequences test it within framework authorship attribution problem....

10.48550/arxiv.2306.05186 preprint EN other-oa arXiv (Cornell University) 2023-01-01
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