Abstract: A fly ash-based fire-prevention and extinguishing material with carbon dioxide mineralized and stored, and a preparation method thereof are provided. The preparation method includes: separately weighing 100 parts to 120 parts of water, 1 part to 3 parts of a solid strong alkali, and 20 parts to 40 parts of a fly ash as raw materials, pouring the raw materials into a reactor successively to obtain a resulting mixture, and stirring the resulting mixture at a high rotational speed; adding 10 parts to 20 parts of a solubilizing agent to the reactor, sealing the reactor, introducing carbon dioxide to the reactor at room temperature to maintain a carbon dioxide pressure to obtain a resulting slurry, and stirring the resulting slurry at a high rotational speed; and further introducing carbon dioxide into the reactor to increase the carbon dioxide pressure, and stirring the resulting slurry at a low rotational speed.

Abstract: A data processing system implements a dynamic knowledge distillation process including dividing training data into a plurality of batches of samples and distilling a student model from a teacher model using an iterative knowledge distillation. The process includes instantiating an instance of the teacher model and the student model in a memory of the data processing system and obtaining a respective batch of training data from the plurality of batches of samples in the memory. The process includes training the teacher and student models using each of the samples in the respective batch of the training data, evaluating the performance of the student model compared with the performance of the teacher model, and providing feedback to student model to adjust the behavior of the student model based on the performance of the student model.

Abstract: A system and method and for retrieving one or more visual assets includes receiving a search query for the one or more visual assets, the search query including textual data, encoding the textual data into one or more text embedding representations via a trained text representation machine-learning (ML) model, transmitting the one or more text embedding representations to a matching and selection unit, providing visual embedding representations of one or more visual assets to the matching and selection unit, comparing, by the matching and selection unit, the one or more text embedding representations to the visual embedding representations to identify one or more visual asset search results, and providing the one or more visual asset search results for display.