Abstract: The present disclosure relates to the field of image definition recognition, and discloses a logging image definition recognition method and device, medium and electronic equipment. The method comprises: establishing a logging image sample library comprising a plurality of logging images; acquiring actual definition information corresponding to the respective logging images; acquiring a plurality of definitions corresponding to the respective logging images; determining target weights corresponding to the respective target image definition determination algorithms according to the plurality of definitions and the actual definition information corresponding to the respective logging images; and determining a definition of a target logging image by the respective target image definition determination algorithms and the target weights corresponding to the respective target image definition determination algorithms.

Abstract: Compositions and methods for controlling pests are provided. The methods involve transforming organisms with a nucleic acid sequence encoding an insecticidal protein. In particular, the nucleic acid sequences are useful for preparing plants and microorganisms that possess insecticidal activity. Thus, transformed bacteria, plants, plant cells, plant tissues and seeds are provided. Compositions are insecticidal nucleic acids and proteins of bacterial species. The sequences find use in the construction of expression vectors for subsequent transformation into organisms of interest including plants, as probes for the isolation of other homologous (or partially homologous) genes. The pesticidal proteins find use in controlling, inhibiting growth or killing Lepidopteran, Coleopteran, Dipteran, fungal, Hemipteran and nematode pest populations and for producing compositions with insecticidal activity.

Abstract: A detection device includes a frame, a transport mechanism, detection mechanisms, and a grasping mechanism. The transport mechanism includes a feeding line, a first flow line, and a second flow line arranged in parallel on the frame. The detection mechanisms are arranged on the frame and located on two sides of the transport mechanism. The grasping mechanism is arranged on the frame and used to transport workpieces on the feeding line to the detection mechanisms, transport qualified workpieces to the first flow line, and transport unqualified workpieces to the second flow line.

Abstract: This disclosure relates to the field of artificial intelligence, and discloses a data processing method. The method includes: obtaining a transformer model including a target network layer and a target module; and processing to-be-processed data by using the transformer model, to obtain a data processing result. The target module is configured to: perform a target operation on a feature map output at the target network layer, to obtain an operation result, and fuse the operation result and the feature map output, to obtain an updated feature map output. In this disclosure, the target module is inserted into the transformer model, and the operation result generated by the target module and an input are fused, so that information carried in a feature map output by the target network layer of the transformer model is increased.

Abstract: A method is provided for quantizing a neural network model performed by a processing system. The method comprises determining a scaling factor based on a distribution of weights associated with the neural network model, determining quantized weights based on the scaling factor and the weights associated with the distribution, determining a training loss of the neural network model based on the quantized weights during training of the neural network model, and determining an updated scaling factor for the neural network model based on a gradient of the training loss.

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 model compression method is provided, which can be applied to the field of artificial intelligence. The method includes: obtaining a first neural network model, a second neural network model, and a third neural network model; processing first to-be-processed data using the first neural network model, to obtain a first output; processing the first to-be-processed data using the third neural network model, to obtain a second output; determining a first target loss based on the first output and the second output, and updating the second neural network model based on the first target loss, to obtain an updated second neural network model; and compressing the updated second neural network model to obtain a target neural network model. The model generated based on the method has higher processing precision.

Abstract: A data processing method includes: obtaining to-be-processed data and a target neural network model, where the target neural network model includes a first transformer layer, the first transformer layer includes a first residual branch and a second residual branch, the first residual branch includes a first attention head, and the second residual branch includes a target feed-forward network (FFN) layer; and performing target task related processing on the to-be-processed data based on the target neural network model, to obtain a data processing result, where the target neural network model is for performing a target operation on an output of the first attention head and a first weight value to obtain an output of the first residual branch, and/or the target neural network model is for performing a target operation on an output of the target FFN and a second weight value to obtain an output of the second residual branch.

Abstract: In a data processing method, a processing device obtains a first neural network model and an available resource state of a terminal device, and determines a second neural network model based on the first neural network model and the available resource state. An appropriate model size is determined based on the available resource state, and a part of the first neural network model is selected, based on the determined model size, as the second neural network model on which data processing is to be performed.

Abstract: Neighbor cell planning method and device based on Thiessen polygons are provided. The method includes acquiring working parameter data of cells; adjusting longitudes and latitudes of the cells based on the working parameter data; constructing a Delaunay triangulation based on the adjusted longitudes and latitudes of the cells; generating Thiessen polygons based on the Delaunay triangulation; and calculating neighbor cells of each cell based on the Thiessen polygons and exporting a list of neighbor cells for each cell.