Abstract: Embodiments of the present disclosure relate to multimedia processing method, apparatus, device and medium, wherein the method includes: presenting a first multimedia interface comprising first content; receiving an interface switching request of a user in the first multimedia interface; and switching from the first multimedia interface currently presented to a second multimedia interface, and presenting second content in the second multimedia interface, wherein the first content comprises the second content and other content associated with the second content, and the second content comprises a target audio and a target subtitle corresponding to the target audio.

Abstract: Embodiments herein relate to prediction, based on previous usage of a cloud-based computing resource by a user of one or more users of the cloud-based computing resource, future usage of the cloud-based computing resource. Based on the predicted future usage, embodiments relate to identifying that throttling of access to the cloud-based computing resource is to occur, and notifying the user of the throttling. Other embodiments may be described and/or claimed.

Abstract: An integrated circuit apparatus includes a substrate and a well contact that is disposed on the substrate. The well contact includes first and second source/drain structures that are disposed on the substrate; a metal vertical portion that contacts the substrate immediately between the first and second source/drain structures; inner spacers that electrically insulate the vertical portion from the adjacent source/drain structures; bottom dielectric isolation that electrically insulates the source/drain structures from the substrate; and a well portion that is embedded into the substrate in registry with the vertical portion. The well portion is doped differently than the substrate.

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with a sparse set of field weights associated with feature fields associated with features of the plurality of sets of auction information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. Positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon field weights, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the positive signal probabilities.

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

Abstract: A stator for an n-phase electrical machine includes a plurality of first end connectors of a first connector type arranged to electrically connect first ends of shaped strands arranged in stator slots, and a plurality of second end connectors arranged to electrically connect second ends, thereby forming stator winding sections of the electrical machine. A plurality of further connectors is arranged to connect further first ends and thereby to connect stator winding sections to form at least one of parallel and serial arrangements of stator winding sections. Therein, the further connectors are each manufactured from a piece of sheet metal by cutting and bending, and they each have at least one bridge section in which a surface normal to the original sheet surface is parallel to the machine axis.

Abstract: Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

Abstract: Provided herein are compositions, formulations, and (e.g., oral) dosage forms comprising a compound of Formula (I). In specific instances, such compositions comprise an emulsifier, a solubilizer, a polyethylene glycol, a surfactant, and an antioxidant. In some instances, such compositions are useful for the treatment of fibrosis, cancer, and/or chronic inflammation.

Abstract: One or more computing devices, systems, and/or methods are provided. Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of minimum bid values to win associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, unshaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using a loss function and/or the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine a shaded bid value for submission based upon one or more first feature parameters, of the feature parameters, associated with the second set of features.

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with a sparse set of field weights associated with feature fields associated with features of the plurality of sets of auction information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. Positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon field weights, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the positive signal probabilities.

Abstract: Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

Abstract: One or more computing devices, systems, and/or methods are provided. Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of minimum bid values to win associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, unshaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using a loss function and/or the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine a shaded bid value for submission based upon one or more first feature parameters, of the feature parameters, associated with the second set of features.

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

Abstract: Provided herein are compositions, formulations, and (e.g., oral) dosage forms comprising a compound of Formula (I). In specific instances, such compositions comprise an emulsifier, a solubilizer, a polyethylene glycol, a surfactant, and an antioxidant. In some instances, such compositions are useful for the treatment of fibrosis, cancer, and/or chronic inflammation.

Abstract: The disclosure discloses a detection coil structure based on interlayer coupling and a metal object detection system. The detection coil structure includes: a top sub-detection coil and a bottom sub-detection coil, wherein the top and bottom sub-detection coils are the same in structure and similar or same in size and are orthogonal to each other, and both the outer boundaries and geometric symmetry centers of the detection coils are completely coincident; the top sub-detection coil includes a first terminal, a second terminal, a third terminal and a fourth terminal, and the bottom sub-detection coil includes a fifth terminal, a sixth terminal, a seventh terminal and an eighth terminal; and the first terminal is connected to the second terminal, the seventh terminal is connected to the eighth terminal, and the third terminal and the fourth terminal are respectively connected to the fifth terminal and the sixth terminal.

Abstract: A method of adaptive quantization for a convolutional neural network, includes at least one of receiving an acceptable model accuracy, determining a float value multiply accumulate for the layer based on a float value weight and a float value input, quantizing the float value weight at multiple weight quantization precisions, quantizing the float value input at multiple input quantization precisions, determining a multiply accumulate at multiple multiply accumulate quantization precisions based on the weight quantization precisions and the input quantization precisions, determining multiple quantization errors based on differences between the float value multiply accumulate and the multiple multiply accumulate quantization precisions and selecting one of the multiple weight quantization precisions, one of the multiple input quantization precisions and one of the multiple multiply accumulate quantization precisions based on the predetermined acceptable model accuracy and the multiple quantization errors.

Abstract: The invention relates to a rotor for a synchronous drive motor including a plurality of rotor poles, each rotor pole having at least two magnetic layers arranged radially one behind the other, and each magnetic layer comprising a series of magnetic elements, which are formed by injection molding or casting, and solidification of permanent magnetic material in rotor cavities. According to the invention, at least a portion of the magnetic elements each include projections protruding on both sides in their radially inner halves to introduce centrifugal forces of the magnetic elements into the rotor. This ensures an improved possibility of supporting the magnetic elements where sufficient rotor material is available. As a result, it is possible to prevent the radially outer ends of the magnetic elements from abutting the wall of the rotor as a result of centrifugal forces, which reduces the loads and deformations in the webs and bridges between the magnetic elements or to the outer edge of the rotor.

Abstract: The disclosure discloses a detection coil structure based on interlayer coupling and a metal object detection system. The detection coil structure includes: a top sub-detection coil and a bottom sub-detection coil, wherein the top and bottom sub-detection coils are the same in structure and similar or same in size and are orthogonal to each other, and both the outer boundaries and geometric symmetry centers of the detection coils are completely coincident; the top sub-detection coil includes a first terminal, a second terminal, a third terminal and a fourth terminal, and the bottom sub-detection coil includes a fifth terminal, a sixth terminal, a seventh terminal and an eighth terminal; and the first terminal is connected to the second terminal, the seventh terminal is connected to the eighth terminal, and the third terminal and the fourth terminal are respectively connected to the fifth terminal and the sixth terminal.

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with a sparse set of field weights associated with feature fields associated with features of the plurality of sets of auction information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. Positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon field weights, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the positive signal probabilities.