Abstract: A data retransmission method is provided herein. The method includes sending a data frame to an electronic device and receiving an acknowledgement frame from the electronic device within a transmission opportunity duration, where the acknowledgement frame includes first acknowledgement information; and when the first acknowledgement information indicates that at least a part of data in the data frame needs to be retransmitted and further indicates a retransmission period required to complete the retransmission, retransmitting information related to the at least the part of the data to the electronic device in the retransmission period.

Abstract: A method for route handling of a regional centralized control station is based on a CTC3.0 technology, and includes the following steps: step (1): generating multi-station train and shunting plans according to a multi-station planning terminal, and executing steps (2) and (4) at the same time; step (2): sending the train plan to an autonomous computer to generate a train route sequence, and executing step (3); step (3): sending, by the autonomous computer, the train route sequence to a regional centralized control route handling terminal; step (4): compiling, by the multi-station planning terminal, an automatically generated shunting route sequence in the shunting plan, and executing step (5); and step (5): synchronizing, by a center service, the shunting route sequence to the regional centralized control route handling terminal.

Abstract: Methods, electronic devices, and chip systems relating to interaction between devices are provided. A method includes: a first electronic device sends a first sound wave signal and a second sound wave signal to a second electronic device by using a first speaker and a second speaker. The first electronic device receives relative position information between the second electronic device and the first electronic device sent by the second electronic device. The relative position information is determined by a receiving result of receiving the first sound wave signal and the second sound wave signal by a first microphone of the second electronic device.

Abstract: A method is provided for data processing performed by a processing system. The method comprises determining a set of first tokens for first data and a set of second token for second data, each token comprising information associated with a segment of the respective data, determining pair-wise similarities between the set of first tokens and the set of second tokens, each pair comprising a first token in the set of first tokens and a second token in the set of second tokens, determining, for each first token in the set of first tokens, a maximum similarity based on the determined pair-wise similarities between the respective first token and the second tokens in the set of second tokens, and determining a first similarity between the first data and the second data by aggregating the maximum similarities corresponding to the first tokens in the set of first set of tokens.

Abstract: A purified oligosaccharide compound having antithrombotic activity or a mixture of a homologous compound thereof and a pharmaceutically acceptable salt thereof, a preparation method for the mixture, a pharmaceutical composition containing the mixture, and uses thereof serving as an intrinsic factor X-enzyme (Xase) inhibitor in the preparation of drugs for preventing and/or treating thrombotic diseases.

Abstract: A data encoding method includes obtaining to-be-encoded data; processing the to-be-encoded data by using a volume preserving flow model to obtain a hidden variable output, where the volume preserving flow model includes a target volume preserving flow layer, an operation corresponding to the target volume preserving flow layer is an invertible operation that meets a volume preserving flow constraint, the target volume preserving flow layer is used to perform a multiplication operation on a preset coefficient and first data input to the target volume preserving flow layer, and the preset coefficient is not 1; and encoding the hidden variable output to obtain encoded data.

Abstract: A purified oligosaccharide compound having antithrombotic activity or a mixture of a homologous compound thereof and a pharmaceutically acceptable salt thereof, a preparation method for the mixture, a pharmaceutical composition containing the mixture, and uses thereof serving as an intrinsic factor X-enzyme (Xase) inhibitor in the preparation of drugs for preventing and/or treating thrombotic diseases.

Abstract: An H-type molecular sieve having the CHA framework type is directly prepared through hydrothermal synthesis by using the compounds with the chemical formulas (C4H8NO)3PO and/or (C4H8NO)2PO(C4H10N) as structure-directing agents. The structure-directing agent has a molecular topological structure, which is conducive to quickly and efficiently building a molecular sieve having the CHA framework type, so that the prepared product has a regular morphology and high relative crystallinity. The synthesis method can directly synthesize the H-type molecular sieve having the CHA framework type with a relative crystallinity greater than 95%. The molecular sieve product can be obtained by direct drying and calcination without an ammonium exchange process, so the synthesis method is simple, easy to perform, and suitable for large-scale production.

Abstract: The present disclosure provides msect-4 molecular sieves with OFF and ERI topologies, a preparation method therefor, and applications thereof. An eight-membered ring small pore molecular sieve used as a raw material is dispersed in an aqueous phase. Following that, caustic potash, an aluminum source, and an organic structure-directing agent (OSDA) are added. The pH value is then adjusted to be greater than 10, and a silicon source is introduced to attain the desired silicon-aluminum ratio, followed by stirring reaction, aging, crystallization, filtration, washing, ammonia exchange reaction, drying, and calcination. The msect-4 molecular sieves with OFF and ERI topologies, the preparation method therefor, and applications exhibit excellent hydrothermal stability, a plurality of adsorption sites exposed by a regular bone-like structure, and a large specific surface area.

Abstract: The present disclosure provides molecular sieves with intergrown phases of AEI and CHA topologies and a catalyst thereof. A preparation method for the molecular sieves include the following steps: mixing a hydroxyphosphono organic alkali R with an aluminum source and a silicon source to obtain a sol-gel precursor, putting the sol-gel precursor into a closed hydrothermal synthesis reactor for reaction, filtering the reaction solution, washing, drying, and calcination to obtain the molecular sieves with intergrown phases of AEI and CHA topologies. The molecular sieves and the catalyst thereof can be directly synthesized under mild conditions with a hydroxyphosphono organic alkali as a structure-directing agent and a phosphorus source, have a pH value of 6-9 and low requirements for corrosion resistance of production devices, and are suitable for large-scale production.

Abstract: Embodiments of this application provide a data processing method and apparatus to better learn a vector representation value of each feature value in a continuous feature. The method specifically includes: The data processing apparatus obtains the continuous feature from sample data, and then performs discretization processing on the continuous feature by using a discretization model, to obtain N discretization probabilities corresponding to the continuous feature. The N discretization probabilities correspond to N preset meta-embeddings, and N is an integer greater than 1. Finally, the data processing apparatus determines a vector representation value of the continuous feature based on the N discretization probabilities and the N meta-embeddings.

Abstract: The method includes: obtaining a plurality of pieces of feature data; automatically performing two different types of nonlinear combination processing operations on the plurality of pieces of feature data to obtain two groups of processed data, where the two groups of processed data include a group of higher-order data and a group of lower-order data, the higher-order data is related to a nonlinear combination of m pieces of feature data in the plurality of pieces of feature data, and the lower-order data is related to a nonlinear combination of n pieces of feature data in the plurality of pieces of feature data, where m?3, and m>n?2; and determining prediction data based on a plurality of pieces of target data, where the plurality of pieces of target data include the two groups of processed data.

Abstract: A device for machine learning is provided, including a first neural network layer, a second neural network layer with a normalization layer arranged in between. The normalization layer is configured to, when the device is undergoing training on a batch of training samples, receive multiple outputs of the first neural network layer for a plurality of training samples of the batch, each output comprising multiple data values for different indices on a first dimension and a second dimension; group the outputs into multiple groups based on the indices on the first and second dimensions; form a normalization output for each group which are provided as input to the second neural network layer. According to the application, the training of a deep convolutional neural network with good performance that performs stably at different batch sizes and is generalizable to multiple vision tasks is achieved, thereby improving the performance of the training.

Abstract: A deep learning training method includes obtaining a training set, a first neural network, and a second neural network, where shortcut connections included in the first neural network are less than shortcut connections included in the second neural network; performing at least one time of iterative training on the first neural network based on the training set, to obtain a trained first neural network, where any iterative training includes: using a first output of at least one first intermediate layer in the first neural network as an input of at least one network layer in the second neural network, to obtain an output result of the at least one network layer; and updating the first neural network according to a first loss function.

Abstract: A neural network building method and apparatus are disclosed, and relate to the field of artificial intelligence. The method includes: initializing a search space and a plurality of building blocks, where the search space includes a plurality of operators, and the building block is a network structure obtained by connecting a plurality of nodes by using the operator; during training, in at least one training round, randomly discarding some operators, and updating the plurality of building blocks by using operators that are not discarded; and building a target neural network based on the plurality of updated building blocks. In the method, some operators are randomly discarded. This breaks association between operators, and overcomes a co-adaptation problem during training, to obtain a target neural network with better performance.

Abstract: This disclosure discloses lane line detection methods and devices. In an implementation, features extracted by different layers of the neural network are fused to obtain a fused second feature map, so that the second feature map obtained through fusion processing has a plurality of layers of features. The fused second feature map has a related feature of a low-layer receptive field and a related feature of a high-layer receptive field. Afterwards, an output predicted lane line set is divided into groups, where each predicted lane line in each group has an optimal prediction interval.

Abstract: A neural network construction method and apparatus in the field of artificial intelligence, to accurately and efficiently construct a target neural network. The constructed target neural network has high output accuracy, may be further applied to different application scenarios, and has a strong generalization capability. The method includes: obtaining a start point network, where the start point network includes a plurality of serial subnets; performing at least one time of transformation on the start point network based on a preset first search space to obtain a serial network, where the first search space includes a range of parameters used for transforming the start point network; and if the serial network meets a preset condition, training the serial network by using a preset dataset to obtain a trained serial network; and if the trained serial network meets a termination condition, obtaining a target neural network based on the trained serial network.

Abstract: A neural network training method, an image classification system, and a related device, which may be applied to the artificial intelligence field. Feature extraction is performed on images in a training set (including a first set and a second set) by using a prototype network, to obtain first feature points, in a feature space, of a plurality of images in the first set and second feature points of a plurality of images in the second set. The first feature points are used for calculating a prototype of a class of an image, and the second feature points are used for updating a network parameter of the prototype network. A semantic similarity between classes of the images in the second set is obtained, to calculate a margin value between the classes of the images. Then, a loss function is adjusted based on the margin value.

Abstract: A neural network construction method and system in the field of artificial intelligence, to construct a target neural network by replacing a part of basic units in an initial backbone network with placeholder modules, so that different target neural networks can be constructed based on different scenarios. The method may include obtaining an initial backbone network and a candidate set, replacing at least one basic unit in the initial backbone network with at least one placeholder module to obtain a to-be-determined network, performing sampling based on the candidate set to obtain information about at least one sampling structure, and obtaining a network model based on the to-be-determined network and the information about the at least one sampling structure. The information about the at least one sampling structure may be used for determining a structure of the at least one placeholder module.

Abstract: The present disclosure relates to neural network optimization methods and apparatuses in the field of artificial intelligence. One example method includes sampling preset hyperparameter search space to obtain multiple hyperparameter combinations. Multiple iterative evaluations are performed on the multiple hyperparameter combinations to obtain multiple performance results of each hyperparameter combination. Any iterative evaluation comprises obtaining at least one performance result of each hyperparameter combination, and if a hyperparameter combination meets a first preset condition, re-evaluating the hyperparameter combination to obtain a re-evaluated performance result of the hyperparameter combination. An optimal hyperparameter combination is determined. If the optimal hyperparameter combination does not meet a second preset condition, a preset model is updated, based on the multiple performance results of each hyperparameter combination, for next sampling.