Abstract: A method for processing synthetic features is provided, and includes: the synthetic features to be evaluated and original features corresponding to the synthetic features are obtained. A feature extraction is performed on the synthetic features to be evaluated based on a number S of pre-trained samples, to obtain meta features with S samples. S is a positive integer. The meta features are input into the pre-trained meta feature evaluation model for a binary classification prediction, to obtain a probability of binary classification. Quality screening is performed on the synthetic features to be evaluated according to the probability of the binary classification, to obtain second synthetic features to be evaluated. The second synthetic features are classified in a good category. The second synthetic features and original features are input into a first classifier for evaluation. classified in a poor category.

Abstract: A method for data processing is provided. The method includes obtaining first retrieving data associated with a first user and a first retrieving result selected by the first user from at least one retrieving result corresponding to the first retrieving data. The first retrieving data is labelled with an intention tag indicating a retrieving intention of the first user. The method further includes obtaining second retrieving data that is used by a second user to conduct retrieving and selecting the first retrieving result within a predetermined time period. The method further includes assigning the intention tag to the second retrieving data.

Abstract: A technical solution relates to a big data technology in the field of artificial intelligence technologies. The technical solution includes: acquiring training data including annotation results of a risk grade of each sample region and a risk grade of a district to which each sample region belongs; and training an initial model including an encoder, a discriminator and a classifier using the training data, and obtaining the risk prediction model using the encoder and the classifier after the training process. The encoder performs a coding operation using region features of the sample regions to obtains a feature representation of each sample region; the discriminator identifies the risk grade of the district to which the sample region belongs according to the feature representation of the sample region; the classifier identifies the risk grade of the sample region according to the feature representation of the sample region.

Abstract: A method and an apparatus for training a classification model and data classification includes: obtaining a sample set and a pre-trained classification model, wherein the classification model includes at least two convolutional layers, each convolutional layer is connected to a classification layer through a fully connected layer; inputting the sample set into the classification model, and obtaining a prediction result output by each classification layer, wherein the prediction result includes a prediction probability of a class to which each sample belongs; calculating a probability threshold of each classification layer based on the prediction result output by each classification layer; setting a prediction stopping condition for the classification mode according to the probability threshold of each classification layer.

Abstract: The disclosure provides a multi-task deployment method, and an electronic device. The method includes: obtaining N first tasks and K network models, in which N and K are positive integers greater than or equal to 1; allocating the N first tasks to the K network models differently for operation, to obtain at least one candidate combination of tasks and network models, in which each candidate combination includes a mapping relation between the N first tasks and the K network models; selecting a target combination with a maximum combination operation accuracy from the at least one candidate combination; and deploying a target mapping relation comprised in the target combination and the K network models on a prediction machine.

Abstract: The present disclosure discloses an information processing method and device, and relates to the field of artificial intelligence, in particular, to a graph neural network in the field of deep learning. A specific implementation solution according to an embodiment includes determining initial representation of edges connected between a plurality of atoms in a molecule based on three-dimensional structure information of the molecule; determining first representation of a neighbor edge of each of the atoms based on the initial representation of the edges, the neighbor edge of each of the atoms indicating at least one edge connected with each of the atoms; determining first representation of each of the atoms based on the first representation of the neighbor edge of each of the atoms; determining feature representation for characterizing the molecule based on the first representation of each of the atoms.

Abstract: A method for data processing is provided. The method includes obtaining first retrieving data associated with a first user and a first retrieving result selected by the first user from at least one retrieving result corresponding to the first retrieving data. The first retrieving data is labelled with an intention tag indicating a retrieving intention of the first user. The method further includes obtaining second retrieving data that is used by a second user to conduct retrieving and selecting the first retrieving result within a predetermined time period. The method further includes assigning the intention tag to the second retrieving data.

Abstract: The present disclosure provides a method and apparatus for generating an electronic map, an electronic device and a storage medium, and relates to the field of data processing technology. A specific implementation comprises: establishing a plurality of groups of corresponding relationships between enterprise names and enterprise addresses using network data; determining respectively a fine-grained region where each enterprise address is located; and creating an enterprise electronic map of the fine-grained region based on the corresponding relationships between the enterprise names and the enterprise addresses and the fine-grained region where the each enterprise address is located. A corresponding relationship between an enterprise name and an enterprise address is established using existing network data.

Abstract: A double-channel fluid injection apparatus includes a main shaft, a bearing box, and a sealing box. The main shaft includes an outer tube and an inner tube. An internal channel of the inner tube and a vertical through channel constitute a first fluid passageway. The inner tube and the outer tube form an annular gap, and a channel D is radially formed at a lower part of the outer tube and is in communication with the annular gap, thus constituting a second fluid passageway. The bearing box is mounted on a boss of the outer tube. The sealing box includes a sealing cylinder fixedly connected to a lower end of a lower end cap of the bearing box. A channel E in communication with the second fluid passageway is radially formed at a position of the sealing cylinder corresponding to the channel of the outer tube.

Abstract: A computer-implemented method for text classification is provided. The method for text classification includes obtaining an entity category set and a part-of-speech tag set associated with a text. The method further includes constructing a first isomorphic graph for the entity category set and a second isomorphic graph for the part-of-speech tag set. A node of the first isomorphic graph corresponds to an entity category in the entity category set, and a node of the second isomorphic graph corresponds to a part-of-speech tag in the part-of-speech tag set. The method further includes obtaining, based on the first isomorphic graph and the second isomorphic graph, a first text feature and a second text feature of the text through a graph neural network. The method further includes classifying the text based on a fused feature of the first text feature and the second text feature.

Abstract: Disclosed is a data accelerated processing system including a processing device, a storage device, an interface device and a control device. The processing device is configured to realize accelerated operation processing of data. The storage device is electrically connected to the processing device for storing the data sent by a server. The interface device is electrically connected to the processing device for data transmission between the processing device and the server. The control device is configured to regulate the status of the processing device. During an operation process, a large number of operating tasks in the server may be transmitted to the processing device for operating through the interface device, and large amounts of buffered data may be stored in the storage device. The data accelerated processing system improves data reading speed and operation efficiency through the cooperation of the processing device, the storage device and the interface device.

Abstract: Disclosed is a heat dissipation device including a base, a plurality of heat pipes and a heat dissipation structure. The base includes a support plate, a plurality of through-pipe grooves are arranged in the support plate. Each heat pipe includes a heat absorption section and a heat transfer section connected to the heat absorption section, where the heat absorption section is arranged in the through-pipe grooves and at least one heat transfer section of the heat pipe is away from the support plate in the perpendicular direction to the support plate. The heat dissipation structure is arranged on the first surface of the support plate, and the heat transfer section of the plurality of heat pipes extends into the interior of the heat dissipation structure and thermally contacts the heat dissipation structure. The heat dissipation device of the present disclosure dissipates heat faster.

Abstract: Disclosed is a turbo fan including a rotor housing and a fan blade structure. The fan blade structure is arranged around the rotor housing and fixed to the periphery of the rotor housing and includes multiple spaced fan blades extending in the same direction. A section of the fan blade is an arc and the length of the arc is ?-? of the circumference of a circle in which the arc is located. The turbo fan sets the fan blade with an arc section, and when the turbo fan rotates in the direction corresponding to a recess of the fan blade, the arc structure of the fan blade can push more air to increase air volume.

Abstract: Disclosed is a data accelerated processing system including a processing device, a storage device, an interface device and a control device. The processing device is configured to realize accelerated operation processing of data. The storage device is electrically connected to the processing device for storing the data sent by a server. The interface device is electrically connected to the processing device for data transmission between the processing device and the server. The control device is configured to regulate the status of the processing device. During an operation process, a large number of operating tasks in the server may be transmitted to the processing device for operating through the interface device, and large amounts of buffered data may be stored in the storage device. The data accelerated processing system improves data reading speed and operation efficiency through the cooperation of the processing device, the storage device and the interface device.