A research team, led by Prof. MENG Qingyan from the Aerospace Information Research Institute of the Chinese Academy of Sciences, has successfully developed the Global Spatiotemporal Fusion Model (GLOSTFM), a high-efficiency spatiotemporal fusion model that utilizes multi-source satellite data. By integrating thermal infrared and microwave observations from the Fengyun-3D satellite, GLOSTFM enhances the spatiotemporal resolution of land surface temperature data.
A research team from the Xinjiang Technical Institute of Physics and Chemistry of the Chinese Academy of Sciences, has made strides in the theoretical design of nonlinear optical (NLO) materials by leveraging machine learning techniques.
A research team from the Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences has introduced a novel feature selection method by removing noise entropy within mutual information.
A groundbreaking multi-task learning framework, DEMENTIA, has been developed by Prof. LI Hai and his team at the Hefei Institutes of Physical Science, to improve the early detection and assessment of Alzheimer's disease.
Researchers from the Aerospace Information Research Institute of the Chinese Academy of Sciences leveraged the advanced capabilities of SDGSAT-1's Glimmer Imager and Thermal Infrared Spectrometer to monitor gas flaring activities in the South China Sea.
A research team from the Xinjiang Institute of Physics and Chemistry of CAS proposed a graph machine learning model, namely TREE, based on the Transformer framework. With this novel Transformer-based architecture, TREE not only identifies the most influential omics data type but also detects the most representative network paths involved in regulating genes that drive cancer formation and progression.
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