Abstract: Methods of encoding and decoding for video data are described for encoding or decoding coefficients for a transform unit. In particular, the sign bits for the non-zero coefficients are encoded using sign bit hiding. Two or more sets of coefficients are defined for the transform unit and a sign bit may be hidden for each set, subject to satisfaction of a threshold test. The sets may correspond to coefficient groups that are otherwise used in multi-level significance map encoding and decoding.

Abstract: A traffic enforcement system and corresponding method are provided. The traffic enforcement system includes a camera configured to capture an input image of one or more subjects in a motor vehicle. The traffic enforcement system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task on one or more subjects in a motor vehicle depicted in the input image. The traffic enforcement system also includes a processor configured to apply the deep learning model to the input image to recognize an identity the one or more subjects in the motor vehicle and a liveness of the one or more subjects. The liveness detection task is configured to evaluate a plurality of different distractor modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.

Abstract: A machine access control system and corresponding method are provided. The machine access control system includes a camera configured to capture an input image of a subject purported to be a person associated with operating a particular workplace machine. The machine access control system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The machine access control system also includes a processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized to use the particular workplace machine and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distractor modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.

Abstract: A login access control system is provided. The login access control system includes a camera configured to capture an input image of a subject purported to be a person and attempting to login to a system to access secure data. The login access control system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The login access control system also includes a processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the secure data and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distractor modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.

Abstract: A facility access control system and corresponding method are provided. The facility access control system includes a camera configured to capture an input image of a subject attempting to enter or exit a restricted facility. The facility access control system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The facility access control system also includes a processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the facility and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distracter modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.

Abstract: A face recognition system and corresponding method are provided. The face recognition system includes a camera configured to capture an input image of a subject purported to be a person. The face recognition system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The face recognition system also includes a processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distractor modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.

Abstract: Techniques for facial expression capture for character animation are described. In one or more implementations, facial key points are identified in a series of images. Each image, in the series of images, is normalized from the identified facial key points. Facial features are determined from each of the normalized images. Then a facial expression is classified, based on the determined facial features, for each of the normalized images. In additional implementations, a series of images are captured that include performances of one or more facial expressions. The facial expressions in each image of the series of images are classified by a facial expression classifier. Then the facial expression classifications are used by a character animator system to produce a series of animated images of an animated character that include animated facial expressions that are associated with the facial expression classification of the corresponding image in the series of images.

Abstract: Methods and devices for decoding, in a video decoder, a block of enhancement-layer transform domain data for an enhancement-layer encoded video and corresponding to a reconstructed block of reference-layer transform domain data, are provided. The method includes determining a context for an enhancement-layer element based, at least in part, upon elements in the corresponding reconstructed block of reference-layer transform domain data, wherein the elements are identified by a template and wherein the position of the template in the reference-layer reconstructed block is based upon the position of that enhancement-layer element in the block of enhancement-layer transform domain data.

Abstract: The described embodiments relate to methods, systems, and products for providing verification code recovery and remote authentication for a plurality of devices configured for electronic communication with a server. Specifically, in the methods, systems, and products, the user entrusts information about the user's verification code to the service provider, and only with cooperation between the user and the service provider can a lost verification code be recovered. The service provider can further authenticate the user before cooperating in the recovery process by way of a time-sensitive authentication sequence that involves the user device.

Abstract: A process for improving sensitivity of a film base, coated with a dispersion of a normally crystalline polyacetylenic compound in a non-solvating liquid which is dried on the film surface, to particular photon energy band, specifically, long wavelength UV; the polyacetylenic compound preferably having at least two conjugated acetylenic linkages and containing from 12 to 60 carbon atoms. The sensitization of the film to long wavelength UV is achieved via the addition of photoinitiator(s) capable of absorbing UV energy and converting it to free radicals.

Abstract: Disclosed an electricity amount detection method and device, a terminal and a storage medium. The method includes that: Identity (ID) information of each function device called by an application is acquired according to ID information of the application; a power consumption parameter generated when each function device is called is correspondingly acquired according to the ID information of each function device called by the application; a power consumption parameter generated when the application is running is determined according to the power consumption parameter generated by each function device; and the power consumption parameter generated when the application is running is output as a power consumption index when the application is running.

Abstract: A single inductor positive and negative voltage output device, which not only reduces a chip area, but also meets mutual independent application needs of positive and negative voltage output load currents, which includes an inductor L, one end of the inductor L is connected to the drain of a first PMOS power switch M1 and to the drain of a third NMOS power switch M3 respectively, the other end of the inductor L is connected to the drain of a second NMOS power switch M2 and to the source of a fourth PMOS power switch M4 respectively, the gates of M1, M2, M3 and M4 are respectively connected to a drive circuit (1), the source of M1 is connected to a power supply terminal VIN, the source of M2 is connected to a ground terminal (6), the source of M3 is connected to a negative voltage output end VON, the drain of M4 is connected to a positive voltage output end VOP, the negative voltage output end VON is connected to ground terminal by a negative terminal capacitor CON, the positive voltage output end VOP is conne

Abstract: Methods, systems, and computer programs for encoding and decoding image are described. In some aspects, an input data block and a prediction data block are accessed. A projection factor is generated based on a projection of the input data block onto the prediction data block. A scaled prediction data block is generated by multiplying the projection factor by the prediction data block. A residual data block is generated based on a difference between the input data block and the scaled prediction data block. In some aspects, a prediction data block, a residual data block, and a projection factor associated with the residual data block are accessed. A scaled prediction data block is generated by multiplying the projection factor by the prediction data block. An output data block is generated by summing the residual data block and the scaled prediction data block.

Abstract: A system and method are provided. The system includes a processor. The processor is configured to generate a response map for an image, using a four stage convolutional structure. The processor is further configured to generate a plurality of landmark points for the image based on the response map, using a shape basis neural network. The processor is additionally configured to generate an optimal shape for the image based on the plurality of landmark points for the image and the response map, using a point deformation neural network. A recognition system configured to identify the image based on the generated optimal shape to generate a recognition result of the image. The processor is also configured to operate a hardware-based machine based on the recognition result.

Abstract: According to one embodiment, a first keyword-to-image (keyword/image) mapping table is provided. The first keyword mapping table includes a number of entries, each entry mapping a keyword to one or more image identifiers (IDs) identifying one or more images. For each of the keywords of the first keyword/image mapping table, an analysis is performed on the keyword to determine one or more related keywords that are related to the keyword. One or more additional entries corresponding to the one or more related keywords are generated to be incorporated into the first keyword/image mapping table to generate a second keyword/image mapping table. The second keyword/image mapping table is utilized to associated a particular image to a particular content item related to a particular keyword.

Abstract: A silicon particle comprising a silicon body, a functionalized silica surface surrounding the silicon body, and a targeting moiety specifically targeting tumor cells, and, optionally, an enzymatically metabolizable compound, is useful in the treatment of cancer by producing cell death after particle internalization.

Abstract: Methods and devices for reconstructing coefficient levels from a bitstream of encoded video data for a coefficient group in a transform unit. Sign bits are hidden in the parity of partially overlapping subsets of a set of coefficients. This enables the hiding of multiple sign bits per coefficient group. Other information bits may be hidden instead of sign bits in some cases.

Abstract: Block copolymers for use in block copolymer lithography, self-assembled films of the block copolymers and methods for polymerizing the block copolymers are provided. The block copolymers are characterized by high Flory-Huggins interaction parameters (?). The block copolymers can be polymerized from protected hydroxystyrene monomers or from tert-butyl styrene and 2-vinylpyridine monomers.

Abstract: Techniques for facial expression capture for character animation are described. In one or more implementations, facial key points are identified in a series of images. Each image, in the series of images, is normalized from the identified facial key points. Facial features are determined from each of the normalized images. Then a facial expression is classified, based on the determined facial features, for each of the normalized images. In additional implementations, a series of images are captured that include performances of one or more facial expressions. The facial expressions in each image of the series of images are classified by a facial expression classifier. Then the facial expression classifications are used by a character animator system to produce a series of animated images of an animated character that include animated facial expressions that are associated with the facial expression classification of the corresponding image in the series of images.

Abstract: The described embodiments relate to methods, systems, and products for providing verification code recovery and remote authentication for a plurality of devices configured for electronic communication with a server. Specifically, in the methods, systems, and products, the user entrusts information about the user's verification code to the service provider, and only with cooperation between the user and the service provider can a lost verification code be recovered. The service provider can further authenticate the user before cooperating in the recovery process by way of a time-sensitive authentication sequence that involves the user device.