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Polyploidy and subsequent post-polyploid diploidization (PPD) are key drivers of plant genome evolution, yet their contributions to evolutionary success remain debated. Here, we analyze the Malvaceae family as an exemplary system for elucidating the evolutionary role of polyploidy and PPD in angiosperms, leveraging 11 high-quality chromosome-scale genomes from all nine subfamilies, including newly sequenced, near telomere-to-telomere assemblies from four of these subfamilies. Our findings reveal a complex reticulate paleoallopolyploidy history early in the diversification of the Malvadendrina clade, characterized by multiple rounds of species radiation punctuated by ancient allotetraploidization (Mal-β) and allodecaploidization (Mal-α) events around the Cretaceous–Paleogene (K–Pg) boundary. We further reconstruct the evolutionary dynamics of PPD and find a strong correlation between dysploidy rate and taxonomic richness of the paleopolyploid subfamilies (R^2 ≥ 0.90, P < 1e-4), supporting the “polyploidy for survival and PPD for success” hypothesis. Overall, our study provides a comprehensive reconstruction of the evolutionary history of the Malvaceae and underscores the crucial role of polyploidy–dysploidy waves in shaping plant biodiversity. 

Finger-Fitts law [6] is a variant of Fitts’ law which accounts for the finger ambiguity in touch pointing. In this paper we investigated two research questions related to Finger-Fitts law: (1) Should Finger-Fitts law use nominal target width W or effect target width We to model MT? and (2) should Finger-Fitts law use a pre-defined value (denoted by σa) or a free parameter (denoted by c) to represent the absolute ambiguity caused by finger touch? Our investigation on two touch pointing datasets showed that there are cases where using nominal width has stronger model fitness, and also cases where using effective width is better. Regarding the representation of finger ambiguity, using a free parameter c to represent the ambiguity of finger touch always leads to stronger model fitness than using the pre-defined σa, after controlling for overfitting. It indicates that viewing the finger ambiguity as an empirically determined parameter has more flexibility to capture the ambiguity of finger touch involved in the study. Overall, our research advances the understanding on how to model Finger touch input with Finger-Fitts law.