FaceAtlasAR: Atlas of Facial Acupuncture Points in Augmented Reality

Acupuncture is a technique in which practitioners stimulate specific points on the body. Those points, called acupuncture points (or acupoints), anatomically define areas on the skin relative to specific landmarks on the body. However, mapping the acupoints to individuals could be challenging for inexperienced acupuncturists. In this project, we proposed a system to localize and visualize facial acupoints for individuals in an augmented reality (AR) context. This system combines a face alignment model and a hair segmentation model to provide dense reference points for acupoints localization in real-time (60FPS). The localization process takes the proportional bone (B-cun or skeletal) measurement method, which is commonly operated by specialists; however, in the real practice, operators sometimes find it inaccurate due to the skillrelated error. With this system, users, even without any skills, can locate the facial acupoints as a part of the self-training or self-treatment process.

A simple skeletal measurement effectively predicts climbing behaviour in a diverse clade of small mammals

Arboreal locomotion allows access to above-ground resources and might have fostered the diversification of mammals. Nevertheless, simple morphological measurements that consistently correlate with arboreality remain indefinable. As such, the climbing habits of many species of mammals, living and extinct, remain speculative. We collected quantitative data on the climbing tendencies of 20 species of murine rodents, an ecologically and morphologically diverse clade. We leveraged Bayesian phylogenetic mixed models (BPMMs), incorporating intraspecific variation and phylogenetic uncertainty, to determine which, if any, traits (17 skeletal indices) predict climbing frequency. We used ordinal BPMMs to test the ability of the indices to place 48 murine species that lack quantitative climbing data into three qualitative locomotor categories (terrestrial, general and arboreal). Only two indices (both measures of relative digit length) accurately predict locomotor styles, with manus digit length showing the best fit. Manus digit length has low phylogenetic signal, is largely explained by locomotor ecology and might effectively predict locomotion across a multitude of small mammals, including extinct species. Surprisingly, relative tail length, a common proxy for locomotion, was a poor predictor of climbing. In general, detailed, quantitative natural history data, such as those presented here, are needed to enhance our understanding of the evolutionary and ecological success of clades.