The near-Earth carbonaceous asteroid 162173 Ryugu is thought to have been produced from a parent body that contained water ice and organic molecules. The Hayabusa2 spacecraft has obtained global multi-color images of Ryugu. Geomorphological features present include a circum-equatorial ridge, east/west dichotomy, high boulder abundances across the entire surface, and impact craters. Age estimates from the craters indicate a resurfacing age of ≲106 years for the top 1-meter layer. Ryugu is among the darkest known bodies in the Solar System. The high abundance and spectral properties of boulders are consistent with moderately dehydrated materials, analogous to thermally metamorphosed meteorites found on Earth. The general uniformity in color across Ryugu’s surface supports partial dehydration due to internal heating of the asteroid’s parent body.

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Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies.