Abstract: An application suppression method that an electronic device monitors a plurality of application windows. When detecting that at least one application window in the plurality of application windows changes, the electronic device traverses window information of the plurality of application windows, and determines an application window that is invisible to a user. When all windows of a first application are invisible to the user, graphics processing unit (GPU) suppression is performed on the first application, or when all windows of a first process are invisible to the user, GPU suppression is performed on the first process.

Abstract: A search method based on a neural network model is provided. The neural network model includes a semantic representation model, a recall model, and a ranking model. The present disclosure relates to the field of artificial intelligence, and in particular to the technical field of search. An implementation of the method comprises: inputting a target search and a plurality of objects to be matched to the semantic representation model to obtain a first output of the semantic representation model; inputting the first output of the semantic representation model to the recall model, and obtaining at least one recall object matching the target search from the plurality of objects to be matched by using the recall model; and inputting a second output of the semantic representation model to the ranking model, and obtaining a matching value of each of the at least one recall object by using the ranking model.

Abstract: The present disclosure provides a multi-system-based intelligent question answering method and apparatus, and a device, relating to the field of artificial intelligence, in particular to the field of knowledge graph. The specific implementation solution is: determining a question category of question information in response to a question answering instruction of a user, wherein the question answering instruction is used to indicate the question information; determining a query engine corresponding to the question category, and invoking multiple question analysis systems corresponding to the query engine according to the query engine; and feeding back answer information to the user when the answer information corresponding to the question information is determined according to a current question analysis system in a process of processing the question information by sequentially using the multiple question analysis systems according to system priorities of the question analysis systems.

Abstract: The present disclosure relates to a magnon spin valve device, a magnon sensor, a magnon field effect transistor, a magnon tunnel junction and a magnon memory. A magnon spin valve device may comprise a first ferromagnetic insulation layer, a non-magnetic conductive layer disposed on the first ferromagnetic insulation layer, and a second ferromagnetic insulation layer disposed on the non-magnetic conductive layer.

Abstract: An action recognition method includes: obtaining original feature submaps of each of temporal frames on a plurality of convolutional channels by using a multi-channel convolutional layer; calculating, by using each of the temporal frames as a target temporal frame, motion information weights of the target temporal frame on the convolutional channels according to original feature submaps of the target temporal frame and original feature submaps of a next temporal frame, and obtaining motion information feature maps of the target temporal frame on the convolutional channels according to the motion information weights; performing temporal convolution on the motion information feature maps of the target temporal frame to obtain temporal motion feature maps of the target temporal frame; and recognizing an action type of a moving object in image data of the target temporal frame according to the temporal motion feature maps of the target temporal frame on the convolutional channels.

Abstract: A search method based on a neural network model is provided. The neural network model includes a semantic representation model, a recall model, and a ranking model. The present disclosure relates to the field of artificial intelligence, and in particular to the technical field of search. An implementation of the method comprises: inputting a target search and a plurality of objects to be matched to the semantic representation model to obtain a first output of the semantic representation model; inputting the first output of the semantic representation model to the recall model, and obtaining at least one recall object matching the target search from the plurality of objects to be matched by using the recall model; and inputting a second output of the semantic representation model to the ranking model, and obtaining a matching value of each of the at least one recall object by using the ranking model.

Abstract: The present disclosure provides a multi-system-based intelligent question answering method and apparatus, and a device, relating to the field of artificial intelligence, in particular to the field of knowledge graph. The specific implementation solution is: determining a question category of question information in response to a question answering instruction of a user, wherein the question answering instruction is used to indicate the question information; determining a query engine corresponding to the question category, and invoking multiple question analysis systems corresponding to the query engine according to the query engine; and feeding back answer information to the user when the answer information corresponding to the question information is determined according to a current question analysis system in a process of processing the question information by sequentially using the multiple question analysis systems according to system priorities of the question analysis systems.

Abstract: An action recognition method includes: obtaining original feature submaps of each of temporal frames on a plurality of convolutional channels by using a multi-channel convolutional layer; calculating, by using each of the temporal frames as a target temporal frame, motion information weights of the target temporal frame on the convolutional channels according to original feature submaps of the target temporal frame and original feature submaps of a next temporal frame, and obtaining motion information feature maps of the target temporal frame on the convolutional channels according to the motion information weights; performing temporal convolution on the motion information feature maps of the target temporal frame to obtain temporal motion feature maps of the target temporal frame; and recognizing an action type of a moving object in image data of the target temporal frame according to the temporal motion feature maps of the target temporal frame on the convolutional channels.

Abstract: The present disclosure relates to a magnon spin valve device, a magnon sensor, a magnon field effect transistor, a magnon tunnel junction and a magnon memory. A magnon spin valve device may comprise a first ferromagnetic insulation layer, a non-magnetic conductive layer disposed on the first ferromagnetic insulation layer, and a second ferromagnetic insulation layer disposed on the non-magnetic conductive layer.

Abstract: The present disclosure relates to a magnon spin valve device, a magnon sensor, a magnon field effect transistor, a magnon tunnel junction and a magnon memory. A magnon spin valve device may comprise a first ferromagnetic insulation layer, a non-magnetic conductive layer disposed on the first ferromagnetic insulation layer, and a second ferromagnetic insulation layer disposed on the non-magnetic conductive layer.