Abstract: Embodiments of this application disclose an indoor positioning method, a communication system, and a related device. The method in embodiments of this application includes an indoor terminal device receives first location information of a network device and second location information of a reconfigurable intelligent surface, to obtain a first reference signal, where the first reference signal is obtained by the reconfigurable intelligent surface by reflecting a location reference signal from the network device. The indoor terminal device determines a target location of the terminal device based on the first reference signal, the first location information, and the second location information.

Abstract: Executing serverless application functions is provided. A response to a user request for a service is received with an include callback parameter and a transaction identifier of the user request included in a header of the response from an external service via a network. A checkpoint of a container corresponding to the service is retrieved from a data store using the transaction identifier of the user request. The container corresponding to the service is restored using the checkpoint to process the response received from the external service.

Abstract: Embodiments of the disclosure relate to a method, apparatus, device, and medium for information processing, wherein the method includes: in response to a trigger operation for a workflow type, displaying an editing page comprising a workflow identification and/or a workflow content, and the workflow content comprising a document, the document being a co-operable shared document; receiving, on the editing page, an edited content input by a predetermined user; and in response to a submission instruction for the workflow type, transmitting a corresponding workflow to a first user. Therefore, the application of collaboration of a plurality of parties in the workflow enhances the degree of automation and efficiency of workflow processing and satisfies the needs of a plurality of parties to interact in workflow processing.

Abstract: The present invention discloses a novel high-temperature visual acid etching test device and method; the test device includes a visual rock slab holder, an acid solution storage tank I, an acid solution storage tank II, a heating apparatus, a high-pressure water pump, a low-pressure acid pump, a back-pressure valve, an acid valve A, an acid valve B, an acid valve C, an acid valve D, an acid valve E, an acid valve F, a water valve G, a water valve H, a water valve I, a water valve J, and a water valve K; both the acid solution storage tank I and the acid solution storage tank II are provided with a piston therein, where the acid solution storage tank is divided into upper and lower independent parts by the piston. According to the present invention, the flowing process of an acid solution in a fracture can be directly observed to accurately characterize the distribution of the acid solution in the fracture, which facilitates a study on an acid etching mechanism.

Abstract: Methods, systems, and devices for techniques for efficiently handling misaligned sequential reads are described. A memory system may include a memory device that includes multiple memory dies. The memory system may receive a first read command and a second read command from a host system. The first read command may be associated with a first set of physical addresses and the second read command may be associated with a second set of physical addresses. The memory system may determine, based on the first set of physical addresses and the second set of physical addresses, that the first read command and the second read command are for a same memory die of the multiple memory dies. The memory system may then transmit to the memory die a read request that indicates the first set of physical addresses and the second set of physical addresses.

Abstract: Disclosed are a training method and apparatus for a CNN model, which belong to the field of image recognition. The method comprises: performing a convolution operation, maximal pooling operation and horizontal pooling operation on training images, respectively, to obtain second feature images; determining feature vectors according to the second feature images; processing the feature vectors to obtain category probability vectors; according to the category probability vectors and an initial category, calculating a category error; based on the category error, adjusting model parameters; based on the adjusted model parameters, continuing the model parameters adjusting process, and using the model parameters when the number of iteration times reaches a pre-set number of times as the model parameters for the well-trained CNN model. After the convolution operation and maximal pooling operation on the training images on each level of convolution layer, a horizontal pooling operation is performed.

Abstract: Embodiments of this disclosure belong to the field of computer technologies and disclose a method and an apparatus for recognizing a character string in an image.

Abstract: Disclosed are a training method and apparatus for a CNN model, which belong to the field of image recognition. The method comprises: performing a convolution operation, maximal pooling operation and horizontal pooling operation on training images, respectively, to obtain second feature images; determining feature vectors according to the second feature images; processing the feature vectors to obtain category probability vectors; according to the category probability vectors and an initial category, calculating a category error; based on the category error, adjusting model parameters; based on the adjusted model parameters, continuing the model parameters adjusting process, and using the model parameters when the number of iteration times reaches a pre-set number of times as the model parameters for the well-trained CNN model. After the convolution operation and maximal pooling operation on the training images on each level of convolution layer, a horizontal pooling operation is performed.

Abstract: Disclosed are a training method and apparatus for a CNN model, which belong to the field of image recognition. The method comprises: performing a convolution operation, maximal pooling operation and horizontal pooling operation on training images, respectively, to obtain second feature images; determining feature vectors according to the second feature images; processing the feature vectors to obtain category probability vectors; according to the category probability vectors and an initial category, calculating a category error; based on the category error, adjusting model parameters; based on the adjusted model parameters, continuing the model parameters adjusting process, and using the model parameters when the number of iteration times reaches a pre-set number of times as the model parameters for the well-trained CNN model. After the convolution operation and maximal pooling operation on the training images on each level of convolution layer, a horizontal pooling operation is performed.

Abstract: Embodiments of this disclosure belong to the field of computer technologies and disclose a method and an apparatus for recognizing a character string in an image.

Abstract: Disclosed are a training method and apparatus for a CNN model, which belong to the field of image recognition. The method comprises: performing a convolution operation, maximal pooling operation and horizontal pooling operation on training images, respectively, to obtain second feature images; determining feature vectors according to the second feature images; processing the feature vectors to obtain category probability vectors; according to the category probability vectors and an initial category, calculating a category error; based on the category error, adjusting model parameters; based on the adjusted model parameters, continuing the model parameters adjusting process, and using the model parameters when the number of iteration times reaches a pre-set number of times as the model parameters for the well-trained CNN model. After the convolution operation and maximal pooling operation on the training images on each level of convolution layer, a horizontal pooling operation is performed.