See:

"Fine-scale Inference of Ancestry Segments without Prior Knowledge of Admixing Groups

View ORCID ProfileMichael Salter-Townshend, View ORCID ProfileSimon Myers"

"
We present an algorithm for inferring ancestry segments and characterizing admixture events, which involve an arbitrary number of genetically differentiated groups coming together. This allows inference of the demographic history of the species, properties of admixing groups, identification of signatures of natural selection, and may aid disease gene mapping. The algorithm employs nested hidden Markov models to obtain local ancestry estimation along the genome for each admixed individual. In a range of simulations, the accuracy of these estimates equals or exceeds leading existing methods that return local ancestry. Moreover, and unlike these approaches, we do not require any prior knowledge of the relationship between sub-groups of donor reference haplotypes and the unseen mixing ancestral populations. Instead, our approach infers these in terms of conditional "copying probabilities". In application to the Human Genome Diversity Panel we corroborate many previously inferred admixture events (e.g. an ancient admixture event in the Kalash). We further identify novel events such as complex 4-way admixture in San-Khomani individuals, and show that Eastern European populations possess 1-5% ancestry from a group resembling modern-day central Asians. We also identify evidence of recent natural selection favouring sub-Saharan ancestry at the HLA region, across North African individuals. We make available an R and C ++ software library, which we term MOSAIC (which stands for MOSAIC Organises Segments of Ancestry In Chromosomes)."

From his twitter account, Lazaridis seems pretty high on it.

Razib Khan post on it:
https://www.gnxp.com/WordPress/2018/07/26/local-ancestry-deconvolution-made-simpler/

He highlights one of the claims about the 1-5% Central Asian in Russians, Finns, etc. I think even older algorithms found that, so I don't know why that was important to mention in the paper.

Anyway, if it does better than Alder that will be a great improvement, since that program is deeply flawed, imo.