Abstract: Disclosed are an irradiation terminal based on a combination of rotating beam lines and an application thereof, the irradiation terminal comprises a combination of rotating beam lines, a rotating gantry, and an operation room. The combination of rotating beam lines includes a rotator beam line, a horizontal beam line, and an inclined beam line at a certain angle to the ground, and can achieve irradiation at different angles; the combination of rotating beam lines is arranged on the rotating gantry, and beam allocation for a plurality of operation rooms at different azimuth angles can be implemented through rotating a single combination of beam lines by 0-360 degrees by the rotating gantry; a plurality of rotating beam lines can be combined to achieve multi-angle beam irradiation in a single operation room.

Abstract: The present disclosure provides a method for preparing a catalyst for pyrolysis of waste plastics to produce oil, comprising: washing and modifying coal gangue powder with acid, and then placing in an alkaline solution, etching under magnetic stirring for 20-30 minutes, and washing with water until neutral; placing the catalyst washed until neutral in a metal solution, loading the metal by impregnation, and then filtering and washing; then placing the catalyst in the molding machine and adding adhesive and water, to compress into a suitable shape, drying, and finally calcinating to activate to obtain a product. The present disclosure not only solves the problem of waste plastic pollution, but also obtains fuel oil with high valuable products while reducing the cost of waste plastic treatment, and also improves the yield of fuel oil.

Abstract: Disclosed is a few-shot gesture recognition method. The method comprises the following steps: customizing, by a user, gesture categories, and acquiring few samples for each gesture category; inputting the acquired samples into a trained few-shot learning model, extracting a feature vector corresponding to each sample, and synthesizing feature vectors belonging to the same gesture to obtain an average feature vector corresponding to each gesture as a prototype vector; acquiring a corresponding sample for a target gesture implemented by the user, and inputting the sample into the few-shot learning model to obtain a feature vector of the target gesture as a query vector; and calculating similarities between the query vector and prototype vectors of different gestures, and selecting a gesture category corresponding to the prototype vector with the highest similarity as a prediction category of the target gesture.

Abstract: The present disclosure provides a method for generating a recommendation model, a content recommendation method, and a content recommendation apparatus, and an electronic device, and relates to an artificial intelligence field and a deep learning field. The method for generating a recommendation model includes: obtaining a graph training sample set; inputting the graph training sample set into a machine learning model to train the machine learning model, in which the machine learning model includes at least one low-rank graph convolutional network, and the low-rank graph convolutional network includes a complete weight matrix composed of a first low-rank matrix and a second low-rank matrix; in which a training objective of the low-rank graph convolutional network includes a first parameter item, a second parameter item and a non-convex low-rank item; and in responding to detecting that a training end condition is met, determining the machine learning model as a recommendation model.

Abstract: A method of processing a text, a training method, a method of generating a knowledge graph, an electronic device, and a non-transitory computer-readable storage medium are provided, which relates to a field of artificial intelligence, particularly to fields such as deep learning, natural language processing, computer vision, and speech processing. The method of processing a text includes: encoding a text to be processed to obtain a feature information; identifying a plurality of entity information from the text, based on the feature information; generating a word relation tensor based on the feature information; and determining a relation between the plurality of entity information by using the word relation tensor, so as to generate a plurality of relational triplets related to the text.

Abstract: A method for training a click rate prediction model includes: obtaining sample feature information and a label value, in which the sample feature information includes feature information of a sample user and feature information of a target object, and the label value is configured to indicate whether the sample user interacts with the target object; obtaining a plurality of adjacent matrixes for feature interaction by processing the feature information of the target object based on the hypernetwork module; obtaining a click rate prediction value of the sample user on the target object using the prediction module, according to the sample feature information and the plurality of adjacent matrixes; and training the click rate prediction model according to the label value and the click rate prediction value.

Abstract: Disclosed is a few-shot gesture recognition method. The method comprises the following steps: customizing, by a user, gesture categories, and acquiring few samples for each gesture category; inputting the acquired samples into a trained few-shot learning model, extracting a feature vector corresponding to each sample, and synthesizing feature vectors belonging to the same gesture to obtain an average feature vector corresponding to each gesture as a prototype vector; acquiring a corresponding sample for a target gesture implemented by the user, and inputting the sample into the few-shot learning model to obtain a feature vector of the target gesture as a query vector; and calculating similarities between the query vector and prototype vectors of different gestures, and selecting a gesture category corresponding to the prototype vector with the highest similarity as a prediction category of the target gesture.

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 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 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: The present disclosure provides a method for generating a recommendation model, a content recommendation method, and a content recommendation apparatus, and an electronic device, and relates to an artificial intelligence field and a deep learning field. The method for generating a recommendation model includes: obtaining a graph training sample set; inputting the graph training sample set into a machine learning model to train the machine learning model, in which the machine learning model includes at least one low-rank graph convolutional network, and the low-rank graph convolutional network includes a complete weight matrix composed of a first low-rank matrix and a second low-rank matrix; in which a training objective of the low-rank graph convolutional network includes a first parameter item, a second parameter item and a non-convex low-rank item; and in responding to detecting that a training end condition is met, determining the machine learning model as a recommendation model.

Abstract: A method for information completion, an electronic device and a storage medium, related to the fields of artificial intelligence, big data, deep learning and the like, are provided. The method includes: acquiring an actual information form and an initialization information form, wherein the actual information form includes an information form which is filled out by a plurality of users and in which target information is missing, and the initialization information form is an information form in which there is target information at each target information position; performing an adjustment on the initialization information form by utilizing a similarity between the users, a low-rank constraint of the initialization information form and a difference between the initialization information form and the actual information form, to obtain an adjusted information form; and supplementing target information in the adjusted information form to a position, in the actual information form.

Abstract: The present application discloses a method for training a classification model, a classification method, an apparatus and a device. A specific implementation is: acquiring behavior information of multiple users and personal basic information of the multiple users; where categories of at least part of users of the multiple users are known; inputting the personal basic information of the multiple users into a classification model to be trained to obtain feature information of the multiple users and predicted categories of users with known categories; and training the classification model to be trained according to the behavior information of the multiple users, the feature information of the multiple users, the predicted categories of the users with the known categories, and real categories of the users with the known categories, to obtain a trained classification model. The user categories determined by using the classification model are more accurate.

Abstract: A distributed topology system may include a distributed topology engine configured to run on a server device and define a topology consisting of one or more building blocks. Each building block may represent one or more tasks pertaining to an application. The system may also include a distributed topology application configured to run on a client device and automatically execute one or more of the building blocks. A distributed topology database may be used to store the topology.

Abstract: A distributed topology system may include a distributed topology engine configured to run on a server device and define a topology consisting of one or more building blocks. Each building block may represent one or more tasks pertaining to an application. The system may also include a distributed topology application configured to run on a client device and automatically execute one or more of the building blocks. A distributed topology database may be used to store the topology.

Abstract: A method and system for restoring an operating environment in a computer system. A request to install a target operating environment on a computer system is received at an application. The application transmits a first notification to the computer system. In response to the first notification, the computer system automatically retrieves an image, wherein the image comprises the target operating environment. The computer system automatically restores the image on the computer system such that the target operating environment is installed on the computer system. The computer system automatically transmits a second notification to the application when the image is restored. The present invention provides a method and system for automating the installation of an operating environment on a computer system.

Abstract: A method and system for restoring an operating environment in a computer system. A request to install a target operating environment on a computer system is received at an application. The application transmits a first notification to the computer system. In response to the first notification, the computer system automatically retrieves an image, wherein the image comprises the target operating environment. The computer system automatically restores the image on the computer system such that the target operating environment is installed on the computer system. The computer system automatically transmits a second notification to the application when the image is restored. The present invention provides a method and system for automating the installation of an operating environment on a computer system.