Recently, a research team led by Prof. XU Yigang and Prof. LIN Mang from the Guangzhou Institute of Geochemistry of the Chinese Academy of Sciences identified seven olivine-bearing clasts from two grams of lunar regolith returned by the Chang'e-6 mission. Their findings were published in Proceedings of the National Academy of Sciences (PNAS) on Oct. 20.

Scientists from China have developed a highly scalable on-chip photonic neural network that solves key bottlenecks long limiting the progress of optical computing. The team's new architecture, called a partially coherent deep optical neural network (PDONN), achieves unprecedented network depth by using a cascadable nonlinear activation function with positive net gain. This, combined with the innovative use of more accessible, partially coherent light sources (like LEDs) instead of narrow-linewidth lasers , enable s a chip with the largest input size and deepest structure of its kind to date. The chip successfully performed image classification tasks with high accuracy, marking a critical step toward energy-efficient, scalable, and widely accessible optical computing.