The first generations of stars formed under conditions very different from anywhere we can see in the nearby universe today. Astronomers are studying these differences using powerful telescopes that can detect galaxies so far away their light has taken billions of years to reach us.  

Now, an international team of astronomers led by Tom Bakx at Chalmers University of Technology in Sweden has measured the temperature of one of the most distant known star factories. The galaxy, known as Y1, is so far away that its light has taken over 13 billion years to reach us.  

X-ray absorption spectroscopy (XAS) provides valuable information about a material’s properties and electronic states. However, it requires extensive expertise and manual effort for conventional analysis. Now, researchers from Japan have developed a novel artificial intelligence-based approach for analyzing XAS data that can enable rapid, autonomous, and object material identification. This novel approach outperforms the previous studies in terms of higher accuracy, accelerating the development of new materials.

Crystalline-amorphous composites comprise crystalline grains separated by amorphous boundaries. The combined role of grain size (D) and amorphous boundary thickness (l) on material properties has not been explored. Now, writing in National Science Review, a team from the Hong Kong University of Science and Technology reports simulation results of mechanical properties across the (D, l) parameter space. They identify optimal (D, l) values that provide maximum strength while also enhancing ductility, successfully circumventing the classic strength-ductility tradeoff.

Existing 3D scene reconstructions require a cumbersome process of precisely measuring physical spaces with LiDAR or 3D scanners, or correcting thousands of photos along with camera pose information. The research team at KAIST has overcome these limitations and introduced a technology enabling the reconstruction of 3D —from tabletop objects to outdoor scenes—with just two to three ordinary photographs. The breakthrough suggests a new paradigm in which spaces captured by camera can be immediately transformed into virtual environments.

KAIST announced on November 6 that the research team led by Professor Sung-Eui Yoon from the School of Computing has developed a new technology called SHARE (Shape-Ray Estimation), which can reconstruct high-quality 3D scenes using only ordinary images, without precise camera pose information.

Existing 3D reconstruction technology has been limited by the requirement of precise camera position and orientation information at the time of shooting to reproduce 3D scenes from a small number of images. This has necessitated specialized equipment or complex calibration processes, making real-world applications difficult and slowing widespread adoption.

To solve these problems, the research team developed a technology that constructs accurate 3D models by simultaneously estimating the 3D scene and the camera orientation using just two to three standard photographs. The technology has been recognized for its high efficiency and versatility, enabling rapid and precise reconstruction in real-world environments without additional training or complex calibration processes.

While existing methods calculate 3D structures from known camera poses, SHARE autonomously extracts spatial information from images themselves and infers both camera pose and scene structure. This enables stable 3D reconstruction without shape distortion by aligning multiple images taken from different positions into a single unified space.