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Coloquio: "BAYESIAN NETWORKS AND TEMPORAL DATA"

Organized by
Departamento de Matemáticas
Speaker
Concha Bielza
Institution of Origin
Department of Artificial Intelligence Technical University of Madrid
Date
01-10-2021
Time
11:30 h
Place
Aula Azul, ICMAT
Description

This talk will describe how Bayesian network models can cope with
temporal data. After a brief introduction to Bayesian networks in static domains,
discrete-time versions will be presented, which include temporal nodes Bayesian
networks, dynamic Bayesian networks and the popular hidden Markov models. The
more recent continuous time Bayesian networks and their supervised classification
counterparts, both in uni- and multi-dimensional settings, will be explained. Real
cases from industry will be used for illustration purposes.
The observation of diffraction features along the two main high symmetry directions allows determining the in plane surface lattice constant with high accuracy. Within experimental error, the in plane lattice parameter of 1T PdTe 2 , 1T PtTe 2 and 2H MoS 2 was found to remain constant in a broad temperature range [2]. Measurements of the surface phonon dispersion curves with HAS at low energies allows determining the substrate bond strength as well as th e b ending rigidity, a piece of information difficult to get with other experimental techniques. I will present results obtained for graphene grown on different metal substrates and sapphire. Finally, I will show how the thermal attenuation of the He specular peak from metal surfaces, described by the Debye Waller exponent, can be directly related to the electron phonon coupling constant λ, also known as the mass correction factor of superconductivity [3]. This makes HAS an ideally suited technique to measure λ in the low energy range (< 0.1 eV) for 2D materials and van der Waals heterostructures . Results obtained recently in our laboratory for 1T PdTe 2 , 1T PtTe 2 and 2H MoS 2 will be presented [ 4].


[1] D. Farías and K. H. Rieder , Rep. Prog . Phys. 61, (1998) 1575; A . Al Taleb and D. Farías , J. Phys.: Condens . Matter 28, (2016) 103005
[2]G. Anemone et al . 2D Materials 5 (3), 035015 (2018 ); G. Anemone et al. 2D Materials 7 (2020) 025007 .
[3] G. Benedek et al. J. Phys. Chem. Lett. 9 (2018) 76; Advanced Materials 32 (2020) 2002072
[4]G. Anemone et al. The Journal of Physical Chemistry C 123 (2019) 3682; G . Anemone et al. npj 2D Materials and Applications 5 (2021) 1 .

Observations

Colloquium ICMAT-UAM-UC3M-UCM

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