Abstract: Embodiments of this application provide a communication method and a related device. The method which is implemented by a terminal device includes: receiving a configuration message, where the configuration message indicates the terminal device to establish at least three radio link control (RLC) entities on a first bearer; establishing the at least three RLC entities on the first bearer based on the configuration message; and performing data transmission through at least one RLC entity on the first bearer. According to the application, data transmission reliability is improved and a data transmission latency is reduced.

Abstract: This disclosure relates to techniques for multi-RAT and DSDA capable wireless devices to handle frame blanking in a wireless communication system. A wireless device may establish wireless links according to a first radio access technology and a second radio access technology. The wireless device may determine to perform transmit and receive blanking for one or more antennas of the wireless device for the first radio access technology to perform sounding reference signal transmissions for the second radio access technology based at least in part on a band combination for the wireless links. The wireless device may determine a modification to channel state feedback reporting for the first radio access technology based at least in part on the transmit and receive blanking. The wireless device may perform channel state feedback reporting using the determined modification.

Abstract: Systems and methods are provided for training a data model based on training data. The training includes pre-training and fine-tuning the data model based on a combination of an autoregressive (AR) model and a non-autoregressive (NAR) model. Training data may be received and encoded into streams of tokens. A pre-trainer during decoding generates a continuum of data structures of the AR and NAR combined model including a main stream and a series of predicting streams. Masked tokens in predicting streams reference or attend to one or more preceding tokens in the main stream or the preceding predicting streams. A fine-tuner selects streams to generate a trained model according to a target data model. The target data model is determined based on balancing an accuracy constraint and an efficiency constraint for predicting tokens. The decoder acts as abridge between the AR and NAR models in generating a trained data model.

Abstract: Functionality is described herein for transforming first and second symbolic linguistic items into respective first and second continuous-valued concept vectors, using a deep learning model, such as a convolutional latent semantic model. The model is designed to capture both the local and global linguistic contexts of the linguistic items. The functionality then compares the first concept vector with the second concept vector to produce a similarity measure. More specifically, the similarity measure expresses the closeness between the first and second linguistic items in a high-level semantic space. In one case, the first linguistic item corresponds to a query, and the second linguistic item may correspond to a phrase, or a document, or a keyword, or an ad, etc. In one implementation, the convolutional latent semantic model is produced in a training phase based on click-through data.

Abstract: Functionality is described herein for transforming first and second symbolic linguistic items into respective first and second continuous-valued concept vectors, using a deep learning model, such as a convolutional latent semantic model. The model is designed to capture both the local and global linguistic contexts of the linguistic items. The functionality then compares the first concept vector with the second concept vector to produce a similarity measure. More specifically, the similarity measure expresses the closeness between the first and second linguistic items in a high-level semantic space. In one case, the first linguistic item corresponds to a query, and the second linguistic item may correspond to a phrase, or a document, or a keyword, or an ad, etc. In one implementation, the convolutional latent semantic model is produced in a training phase based on click-through data.