Abstract: A method for slice-based cell reselection includes: obtaining network slice information and slice-based cell reselection information; deriving a first priority frequency for slice-based cell reselection according to the network slice information and the slice-based cell reselection information; performing cell reselection on the first priority frequency for slice-based cell reselection; and identifying a suitable cell for camping on the first priority frequency for slice-based cell reselection.

Abstract: A method for cell reselection in a Space-Air-Ground Integrated Network, including: monitoring broadcast message, and obtaining coverage information of a serving cell and coverage information of a neighboring cell from the broadcast message, where the serving cell and the neighboring cell are non-terrestrial network cells or terrestrial network cells; determining, in response to determining that the serving cell is a non-terrestrial network cell, location relationship information between the terminal and the serving cell according to the coverage information of the serving cell, and initiating cell reselection measurement according to the location relationship information between the terminal and the serving cell; and, performing cell reselection according to a cell reselection measurement result.

Abstract: The present application discloses a method and apparatus for training a semantic representation model, a device and a computer storage medium, which relates to the field of natural language processing technologies in artificial intelligence.

Abstract: Provided are a method and apparatus for operating a blockchain system, a device and a storage medium. The method is described below. To-be-processed blockchain data is acquired through a kernel engine of a blockchain system. The to-be-processed blockchain data is processed through the kernel engine, and a kernel component interface provided by a component adaptor is called during the processing process of the to-be-processed blockchain data to call a kernel component.

Abstract: The present disclosure provides a text processing method and apparatus, an electronic device and a storage medium, and relates to the field of artificial intelligence technologies such as deep learning and natural language processing. The method may include: configuring, for a to-be-processed text, attention patterns corresponding to heads in a Transformer model using a multi-head-attention mechanism respectively, wherein at least one head corresponds to a different attention pattern from the other N?1 heads, and N denotes a number of heads and is a positive integer greater than 1; and processing the text by using the Transformer model. Model performance and a corresponding text processing effect can be improved by using the solutions according to the present disclosure.

Abstract: Provided are a method and apparatus for operating a blockchain system, a device and a storage medium. The method is described below. To-be-processed blockchain data is acquired through a kernel engine of a blockchain system. The to-be-processed blockchain data is processed through the kernel engine, and a kernel component interface provided by a component adaptor is called during the processing process of the to-be-processed blockchain data to call a kernel component.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for determining a topic. A specific embodiment of the method comprises: determining a to-be-recognized sentence sequence; calculating similarities between the to-be-recognized sentence sequence and each of topic templates in a topic template set in a target area, the each of the topic templates in the topic template set corresponding to a topic in at least one topic in the target area, the topic template including a topic section sequence, and a topic section including a topic sentence sequence; and determining a topic of the to-be-recognized sentence sequence according to an associated parameter, the associated parameter including the similarities between the to-be-recognized sentence sequence and the each of the topic templates in the topic template set. This embodiment reduces labor costs during a topic segmentation.

Abstract: A pre-training method of a neural network model, an electronic device, and a medium. The pre-training data is inputted to the initial neural network model, and the initial neural network model is pre-trained in the first training mode, in the first training mode, the plurality of hidden layers share one hidden layer parameter, and the loss value of the initial neural network model is obtained, if the loss value of the initial neural network model is less than a preset threshold, the initial neural network model continues to be pre-trained in the second training mode, in the second training mode, each of the plurality of hidden layers has its own hidden layer parameter.

Abstract: The present application discloses a method and apparatus for training a semantic representation model, a device and a computer storage medium, which relates to the field of natural language processing technologies in artificial intelligence.

Abstract: The disclosure provides a method for training a semantic analysis model, an electronic device and a storage medium. The method includes: obtaining a plurality of training data, in which each of the plurality of training data comprises a search word, information on at least one text obtained by searching the search word, and at least one associated word corresponding to the at least one text; constructing a graph model based on the training data, and determining target training data from the plurality of training data by using the graph model, the target training data comprising search word samples, information samples and associated word samples; and training a semantic analysis model based on the search word samples, the information samples, and the associated word samples.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for determining a topic. A specific embodiment of the method comprises: determining a to-be-recognized sentence sequence; calculating similarities between the to-be-recognized sentence sequence and each of topic templates in a topic template set in a target area, the each of the topic templates in the topic template set corresponding to a topic in at least one topic in the target area, the topic template including a topic section sequence, and a topic section including a topic sentence sequence; and determining a topic of the to-be-recognized sentence sequence according to an associated parameter, the associated parameter including the similarities between the to-be-recognized sentence sequence and the each of the topic templates in the topic template set. This embodiment reduces labor costs during a topic segmentation.

Abstract: The present invention provides a composition for treating cancers, which comprises (a) pharmaceutically acceptable carriers and (b) an effective amount of an aqueous or aqueous organic solvent extract of a crude preparation comprising Semen Trigonellae, Astragali Radix, Glehniae Radix, and Asparagi Radix. The preferred composition further comprises the extract of other anti-tumor medicinal materials, such as Fructus Ligustri Lucidi, or Herba Selaginellae Doederleinii. The present invention also provides the preparation method and use of the composition in treating cancers, especially lung cancers.

Abstract: The present invention provides a composition for treating cancers, which comprises (a) pharmaceutically acceptable carriers and (b) an effective amount of an aqueous or aqueous organic solvent extract of a crude preparation comprising Semen Trigonellae, Astragali Radix, Glehniae Radix, and Asparagi Radix. The preferred composition further comprises the extract of other anti-tumor medicinal materials, such as Fructus Ligustri Lucidi, or Herba Selaginellae Doederleinii. The present invention also provides the preparation method and use of the composition in treating cancers, especially lung cancers.