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We have developed an artificial intelligence tool, XES Neo, for fitting x-ray emission spectroscopy (XES) data using a genetic algorithm. The Neo package has been applied to extended x-ray absorption fine structure [Terry et al., Appl. Surf. Sci. 547, 149059 (2021)] as well as Nanoindentation data [Burleigh et al., Appl. Surf. Sci. 612, 155734 (2023)] and is in development for x-ray photoelectron spectroscopy data. This package has been expanded to the fitting of XES data by incorporating basic background removal methods (baseline and linear) optimized simultaneously with peak-fitting using the active background approach, as well as the peak shapes Voigt, and an asymmetrical Voigt, known as the Double Lorentzian. The fit parameters are optimized using a robust metaheuristic method, which starts with a population of temporary solutions known as the chromosomes. This population is then evaluated and assigned a fitness score, from which the best solution is then found. Future generations are created through crossover of the best sets of parameters along with some random parameters. Mutation is then done on the new generation using random perturbations to the chromosomal parameters. The population is then evaluated again, and the process continues. The analyzed data presented here are available in the corresponding XESOasis discussion forum (https://xesoasis.org/ai_posted).
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Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo
It is a pleasure to congratulate Ni et al. (Stat Methods Appl 490:1–32, 2021) on the recent advances in Bayesian graphical models reviewed in Ni et al. (Stat Methods Appl 490:1–32, 2021). The authors have given considerable thought to the construction and estimation of Bayesian graphical models that capture salient features of biological networks. My discussion focuses on computational challenges and opportunities along with priors, pointing out limitations of the Markov random field priors reviewed in Ni et al. (Stat Methods Appl 490:1–32, 2021) and exploring possible generalizations that capture additional features of conditional independence graphs, such as hub structure and clustering. I conclude with a short discussion of the intersection of graphical models and random graph models.
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- PAR ID:
- 10303340
- Date Published:
- Journal Name:
- Statistical Methods & Applications
- ISSN:
- 1618-2510
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10260-021-00600-7
