Abstract: The present disclosure provides a Ground Penetrating Radar (GPR) detection imaging method and device for a development condition of a frozen wall, and processing equipment. The method is used to additionally consider the correlation between signals of each channel based on the signals collected by GPR detection, and realize secondary imaging through a cross-correlation back projection method to avoid interference caused by complex conditions of an electromagnetic interference detection environment such as shield segments. This can effectively improve the resolution and imaging accuracy, help to more clearly complete the identification of the development condition of the frozen wall, and meet a high-quality data usage requirement of rock engineering involving Artificial Ground Freezing method (AGF).

Abstract: Transaction data associated with a user is identified. A first embedding representing the transaction data is generated using a deep learning machine learning (ML) model. A second embedding interpretable by a large language model is generated using an adaptor ML model based on the first embedding. A text summary associated with one or more transactions is identified. A third embedding representing the text summary is generated using the large language model. An adaptor ML model is trained based on a similarity between the second embedding and the third embedding.

Abstract: The technology described herein relates to systems, methods, and computer storage media, among other things, for determining whether an electronic transmission (e.g., associated with an electronic payment transaction) should be blocked (e.g., based on being a fraudulent transaction). In embodiments, a policy-based reinforcement learning risk decision agent is used to make these determinations for a plurality of stages associated with the electronic payment transaction (e.g., a pre-authorization stage, a post-authorization stage, and a delay-captured stage). The policy-based reinforcement learning risk decision agent can be trained using previous electronic payment transaction data for previous electronic payment transactions. For example, this particular agent can be trained using pre-authorization electronic payment transaction data, post-authorization electronic payment transaction data, and delay-captured electronic payment transaction data for each of the previous electronic payment transactions.

Abstract: Various embodiments described herein support or provide operations including identifying a machine-learning (ML) model associated with an omni-view knowledge graph; generating an embedding vector that represents the omni-view knowledge graph; identifying a ML model associated with a temporal-view knowledge graph; generating an embedding vector that represents the temporal-view knowledge graph; and training a ML model based on the generated embedding vectors.

Abstract: A tax category prediction model, which is trained using a tax category prediction dataset, provides a tax category prediction for an item having an item listing. The tax category prediction model enhances the ability and accuracy of identifying a tax category associated with an item for sale via the online marketplace (e.g., identifying the tax category before the item is offered for sale via the online marketplace). The tax category prediction dataset is generated based on one or more of a text embedding, an image embedding, another type of embedding, or a combination thereof. The text embedding may be identified by applying a natural language processing model to a text string of the item listing. The natural language processing model may comprise bidirectional encoder representations from transformers (BERT).