With the development of large-scale sky surveys, an increasing number of stellar photometric images have been obtained. However, most stars lack spectroscopic data, which hinders stellar classification. Vision Transformer (ViT) has shown superior performance in image classification tasks compared to most convolutional neural networks (CNNs). In this study, we propose an stellar classification network based on the Transformer architecture, named stellar-ViT, aiming to efficiently and accurately classify the spectral class for stars when provided with photometric images. By utilizing RGB images synthesized from photometric data provided by the Sloan Digital Sky Survey (SDSS), our model can distinguish the seven main stellar categories: O, B, A, F, G, K, and M. Particularly, our stellar-ViT-gri model, which reaches an accuracy of 0.839, outperforms traditional CNNs and the current state-of-the-art stellar classification network SCNet when processing RGB images synthesized from the gri bands. Furthermore, with the introduction of urz band data, the overall accuracy of the stellar-ViT model reaches 0.863, further demonstrating the importance of additional band information in improving classification performance. Our approach showcases the effectiveness and feasibility of using photometric images and Transformers for stellar classification through simple data augmentation strategies and robustness analysis of training dataset sizes. The stellar-ViT model maintains good performance even in small sample scenarios, and the inclusion of urz band data reduces the likelihood of misclassifying samples as lower-temperature subtypes.

The model organism Drosophila melanogaster, as a species of Holometabola, undergoes a series of transformations during metamorphosis. To deeply understand its development, it is crucial to study its anatomy during the key developmental stages. We describe the anatomical systems of the thorax, including the endoskeleton, musculature, nervous ganglion, and digestive system, from the late pupal stage to the adult stage, based on micro-CT and 3D visualizations. The development of the endoskeleton causes original and insertional changes in muscles. Several muscles change their shape during development in a non-uniform manner with respect to both absolute and relative size; some become longer and broader, while others shorten and become narrower. Muscular shape may vary during development. The number of muscular bundles also increases or decreases. Growing muscles are probably anchored by the tissues in the stroma. Some muscles and tendons are absent in the adult stage, possibly due to the hardened sclerites. Nearly all flight muscles are present by the third day of the pupal stage, which may be due to the presence of more myofibers with enough mitochondria to support flight power. There are sexual differences in the same developmental period. In contrast to the endodermal digestive system, the functions of most thoracic muscles change in the development from the larva to the adult in order to support more complex locomotion under the control of a more structured ventral nerve cord based on the serial homology proposed herein.

Hindwing venation is one of the most important morphological features for the functional and evolutionary analysis of beetles, as it is one of the key features used for the analysis of beetle flight performance and the design of beetle-like flapping wing micro aerial vehicles. However, manual landmark annotation for hindwing morphological analysis is a time-consuming process hindering the development of wing morphology research. In this paper, we present a novel approach for the detection of landmarks on the hindwings of leaf beetles (Coleoptera, Chrysomelidae) using a limited number of samples. The proposed method entails the transfer of a pre-existing model, trained on a large natural image dataset, to the specific domain of leaf beetle hindwings. This is achieved by using a deep high-resolution network as the backbone. The low-stage network parameters are frozen, while the high-stage parameters are re-trained to construct a leaf beetle hindwing landmark detection model. A leaf beetle hindwing landmark dataset was constructed, and the network was trained on varying numbers of randomly selected hindwing samples. The results demonstrate that the average detection normalized mean error for specific landmarks of leaf beetle hindwings (100 samples) remains below 0.02 and only reached 0.045 when using a mere three samples for training. Comparative analyses reveal that the proposed approach out-performs a prevalently used method (i.e., a deep residual network). This study showcases the practicability of employing natural images—specifically, those in ImageNet—for the purpose of pre-training leaf beetle hindwing landmark detection models in particular, providing a promising approach for insect wing venation digitization.

This study introduces an automated approach for identifying the tip of the red giant branch (TRGB) in globular clusters, combining astronomical data with algorithmic methods. Using a dataset of 160 globular clusters and Python scripts, we matched stellar sources with Gaia data. Our script generates color-magnitude diagrams (CMDs), and uses the local outlier factor (LOF) algorithm to remove outliers. Applying a second-degree polynomial to fit red giant branch (RGB), we identify the TRGB as the star closest to the fitted curve’s endpoint. By this methdod, we expanded TRGB samples in global clusters to 91 with newer observa tional data. Our results show a decreasing trend in I-band luminosity for metallicities greater than −1, consistent with previous studies. The results show a robust trend fitting and the MI of TRGB is about −4.02 with extremely low metallicity. Our approach enhances TRGB identification efficiency while providing valuable insights for developing automatic tools in astronomical data analysis.