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A. Consiglio

Publications and source records attributed to A. Consiglio.

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Observation and Control of Moir\'e-Tailored Topological Dirac States

Moir\'e heterostructures provide a powerful framework for tailoring electronic band structures via controlled long-range periodic superlattice potentials. Beyond widely studied moir\'e-tailored flat bands, folded band structures can host emergent Dirac states, which have recently attracted considerable interest. Direct momentum-resolved observation of gapless moir\'e-Dirac quasiparticles, however, is challenging and has so far remained elusive. By performing angle-resolved photoemission spectroscopy measurements on an epitaxial surface-moir\'e structure, we here provide direct spectroscopic evidence of moir\'e-dressed Dirac states with topological character. Driven by the one-dimensional superlattice potential, electrons propagate anisotropically with a weak but massless Dirac dispersion along the confinement direction. The observed band crossings belong to topological nodal lines pinned to the mini-Brillouin zone boundaries. As such, they are enforced and robustly protected by the non-symmorphic symmetry of the superlattice. Finally, we demonstrate that the topological excitations can be almost continuously controlled by tuning the moir\'e lattice periodicity, directly unveiling moir\'e heterostructures as a promising platform for creating and controlling topological moir\'e-Dirac states.

cond-mat.str-el

A Bayesian Networks Approach to Operational Risk

A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank using only internal loss data, and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters. The algorithm has been validated on synthetic time series. It should be stressed that the practical implementation of the proposed algorithm has a small impact on the organizational structure of a bank and requires an investment in human resources limited to the computational area.

q-fin.RM