SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost
Peer-Reviewed Publication
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This study introduces a Semantic Structure-Aware model to enhance Class Activation Mapping for image classification, achieving state-of-the-art performance in weakly-supervised tasks without extra training.
A groundbreaking study has uncovered a fascinating connection between the circadian rhythms of tea plants and the microbial communities in their rhizosphere, providing new insights into nutrient cycling.
In a recent Engineering article, researchers Jinghai Li and Li Guo discuss the future of data science and its significance for AI. They point out the challenges in scientific data systems, suggest principles for data collection and processing, and stress the importance of data system logic and architecture for the development of AI and data science.
In a new study published in Engineering, researchers from Huazhong University of Science and Technology and the Technical University of Munich have developed an improved proximal policy optimization (IPPO) method. This method is designed to solve the distributed heterogeneous hybrid blocking flow-shop scheduling problem (DHHBFSP), aiming to minimize total tardiness and total energy consumption. The research offers a practical approach for manufacturing scheduling, and its experimental results show better performance compared with other methods.
In the field of wireless communication, security is of utmost importance. A new study published in Engineering explores intelligent covert communication. It reviews current techniques across different domains, from time and frequency to spatial and modulation. The research also looks at future trends like intelligent cooperative and parasitic covert communication, as well as challenges such as dealing with active detection and integrating sensing and communication. This offers valuable insights for the development of more secure wireless communication methods.
Methanolysis of polyethylene terephthalate to dimethyl terephthalate is a sustainable route for recycling of polyethylene terephthalate (PET) plastic. Herein, we demonstrate that mesoporous Beta zeolite supported zinc oxide (Zn-Beta-meso) is efficient for methanolysis of polyethylene terephthalate to dimethyl terephthalate, exhibiting ~99.9% dimethyl terephthalate yield at 180 °C after reaction for 30 min. Model reactions confirmed that the key step in PET methanolysis was the methanolysis of 2-hydroxyethyl methyl terephthalate to form dimethyl terephthalate, where the highly dispersed zinc species are the active sites for this step. In addition, the Zn-Beta-meso catalyst was active for the methanolysis of various PET substrates. When bottle with pigment, terylene, transparent adhesive tape, and soundproof cotton were applied as the substrates, full PET conversion and higher than 99.0% dimethyl terephthalate yield were obtained.