Abstract: This application discloses a video classification model construction method and apparatus, a video classification method and apparatus, a device, and a medium.

Abstract: A display panel and a display device are provided. The display panel includes a first base substrate; a second base substrate opposite to the first base substrate; a liquid crystal layer between the first and second base substrates; a first alignment film at a side of the first base substrate facing the liquid crystal layer; a second alignment film at a side of the second base substrate facing the liquid crystal layer; a polarizer at a side of the first base substrate away from the liquid crystal layer; and a quarter-wave plate between the polarizer and the first base substrate. An angle between a center line of an included angle between the first alignment direction of the first alignment film and the second alignment direction of the second alignment film and a slow axis of the quarter-wave plate is in a range from 75 to 105 degrees.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.

Abstract: A detachable cryostat includes many novel structures. Two radiation shields are installed in the detachable cryostat. One of the radiation shields is cooled by the second-stage cold chamber utilized to contain a cryogen, and the other one is cooled by the first-stage cold head of the cryocooler. These structures are both used for reducing heat loads from an outside. The resilient supporting device, the resilient circular sleeve, the bellows and the conductive blocks are utilized to achieve excellent thermal contact and complete thermal isolation between the cryocooler and the cryogen. A detachable binary current lead device can be introduced in the detachable cryostat, wherein, the detachable binary current lead includes a superconducting current lead and a copper current lead. When the installation adjustment mechanism is tightly pressed and loosened, it can enable the superconducting current lead to contact and separate from the copper current lead.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.

Abstract: One or more computing devices, systems, and/or methods are provided. A request for content associated with a device and/or a set of request information associated with the request for content may be received. A content item may be transmitted to the device. A set of client information associated with the device may be received. The set of client information may be analyzed to determine a fraudulence label associated with the request for content. Fraud detection information generated based upon the set of request information, the set of client information and/or the fraudulence label may be stored in a fraud detection database. A second request for content associated with a second device and/or a second set of request information associated with the second request for content may be received. A second fraudulence label may be determined based upon the second set of request information and/or the fraud detection database.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.

Abstract: The present teaching relates to method, system, medium, and implementations for task management. When information related to at least one task to be carried out by a user is received, multiple features associated with each of the at least one task are predicted automatically based on a plurality prediction models, derived based on historic information related to the user in carrying out past tasks. The at least one task is then automatically scheduled in a calendar associated with the user based on the multiple features predicted for each of the at least one task to generate an updated calendar with the at least one task scheduled therein.

Abstract: The present teaching relates to method, system, medium, and implementations for learning embeddings. Upon receiving raw event data recording information related to a plurality of events, at least one attribute associated with each of the plurality of events is identified from the raw event data, wherein the at least one attribute represent characteristics associated with the event. The plurality of events are grouped into one or more aggregated groups in accordance with an aggregation criterion, defined with respect to at least some of the attributes identified from the events. Each aggregated group includes some events that satisfies the aggregation criterion which are used to create an event sequence, which includes the events in the aggregated group and one or more gaps each of which separates a pair of adjacent events in the at least one event. The created event sequences are then provided to an artificial neural network (ANN) to learn event embeddings.

Abstract: One or more computing devices, systems, and/or methods are provided. A request for content associated with a device and/or a set of request information associated with the request for content may be received. A content item may be transmitted to the device. A set of client information associated with the device may be received. The set of client information may be analyzed to determine a fraudulence label associated with the request for content. Fraud detection information generated based upon the set of request information, the set of client information and/or the fraudulence label may be stored in a fraud detection database. A second request for content associated with a second device and/or a second set of request information associated with the second request for content may be received. A second fraudulence label may be determined based upon the second set of request information and/or the fraud detection database.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. First representations associated with the first entities and second representations associated with the second entities may be generated based upon the first network profile. Clusters in the first representations and/or the second representations may be identified. One or more coalition networks associated with fraudulent activity may be identified based upon the clusters.

Abstract: This application discloses a video classification model construction method and apparatus, a video classification method and apparatus, a device, and a medium.

Abstract: An image recognition method includes: obtaining an image to be segmented, the image including a plurality of extremum points; generating image feature information according to the image, the image feature information including N image matrices and a heat map, and the heat map being generated according to the plurality of extremum points; determining an image segmentation region corresponding to the image feature information through an image segmentation model, the image segmentation model including N matrix input channels and a heat map input channel, the N matrix input channels having a one-to-one correspondence with the N image matrices, and the heat map input channel having a correspondence with the heat map; and generating an image recognition result of the image according to the image segmentation region.

Abstract: A computer device obtains a to-be-segmented image that includes a plurality of extreme points corresponding to a target object in the to-be-segmented image. The device generates first image feature information according to the to-be-segmented image. The first image feature information includes N image matrices and a first heatmap that is generated according to the extreme points. The device obtains a first image segmentation region corresponding to the first image feature information using a first image segmentation model. The device also obtains a second heatmap according to an annotation point corresponding to the first image segmentation region and the first image segmentation region. The device obtains a second image segmentation region corresponding to the to-be-segmented image using a second image segmentation model. The device generates an image recognition result according to the second image segmentation region.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. First representations associated with the first entities and second representations associated with the second entities may be generated based upon the first network profile. Clusters in the first representations and/or the second representations may be identified. One or more coalition networks associated with fraudulent activity may be identified based upon the clusters.

Abstract: One or more computing devices, systems, and/or methods for determining thresholds are provided. For example, first activity associated with a plurality of client devices may be detected. A first activity distribution associated with the plurality of client devices may be determined based upon the first activity. A plurality of peaks of the first activity distribution may be identified. A plurality of gradients associated with pairs of peaks of the plurality of peaks may be determined. A target peak of the plurality of peaks may be determined based upon the plurality of gradients. A threshold amount of activity associated with the first activity may be determined based upon the target peak. A first set of activity associated with a first client device may be detected. A fraudulence label associated with the first client device may be determined based upon the first set of activity and/or the threshold amount of activity.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with first entities corresponding to a first entity type and second entities associated with a second entity type. A first network profile associated with the first entities and the second entities may be generated based upon the event information. An iterative process may be performed to identify a coalition network associated with fraudulent activity. The iterative process may include analyzing the first network profile to identify a first set of entities, of the first entities, that are related to an entity of the second entities, and/or analyzing the first network profile to identify a second set of entities, of the second entities, that are related to the first set of entities. Multiple iterations may be performed to identify the coalition network.

Abstract: A detachable cryostat includes many novel structures. Two radiation shields are installed in the detachable cryostat. One of the radiation shields is cooled by the second-stage cold chamber utilized to contain a cryogen, and the other one is cooled by the first-stage cold head of the cryocooler. These structures are both used for reducing heat loads from an outside. The resilient supporting device, the resilient circular sleeve, the bellows and the conductive blocks are utilized to achieve excellent thermal contact and complete thermal isolation between the cryocooler and the cryogen. A detachable binary current lead device can be introduced in the detachable cryostat, wherein, the detachable binary current lead includes a superconducting current lead and a copper current lead. When the installation adjustment mechanism is tightly pressed and loosened, it can enable the superconducting current lead to contact and separate from the copper current lead.

Abstract: One or more computing devices, systems, and/or methods are provided. A request for content associated with a device and/or a set of request information associated with the request for content may be received. A content item may be transmitted to the device. A set of client information associated with the device may be received. The set of client information may be analyzed to determine a fraudulence label associated with the request for content. Fraud detection information generated based upon the set of request information, the set of client information and/or the fraudulence label may be stored in a fraud detection database. A second request for content associated with a second device and/or a second set of request information associated with the second request for content may be received. A second fraudulence label may be determined based upon the second set of request information and/or the fraud detection database.