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Communication Dans Un Congrès Année : 2023

From State to Link-Register Model: A transformer for Self-Stabilizing Distributed Algorithms

Résumé

The useful information carried by spatio-temporal data is often outlined by geometric structures and patterns. Filaments or clusters induced by galaxy positions in our Universe are such an example. Two situations are to be considered. First, the pattern of interest is hidden in the data set, hence the pattern should be detected. Second, the structure to be studied is observed, so relevant characterization of it should be done. Probabilistic modelling is one of the approaches that allows to furnish answers to these questions. This is done by developing unitary methodologies embracing simultaneously three directions: modelling, simulation, and inference. This talk presents the use of marked point processes applied to such structures detection and characterization. Practical examples are also shown.
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Dates et versions

hal-04388668 , version 1 (11-01-2024)

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  • HAL Id : hal-04388668 , version 1

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Johanne Cohen, George Manoussakis, Laurence Pilard. From State to Link-Register Model: A transformer for Self-Stabilizing Distributed Algorithms. 25th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, Sep 2023, Nancy, France. ⟨hal-04388668⟩
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