Abstract: A dynamic vision sensor (DVS) color camera (DVS-CCam) operable to acquire a color image of a scene, the DVS-CCam including a DVS photosensor comprising DVS pixels, an illuminator operable to transmit a light pattern characterized by temporal changes in intensity and color to illuminate a scene, an optical system configured to collect and focus on the pixels of the DVS photosensor light reflected by features in the scene from the light pattern transmitted by the illuminator, and a processor configured to process DVS signals generated by the DVS pixels responsive to temporal changes in the reflected light to provide a color image of the scene.

Abstract: Systems, methods, and computer readable media for identifying a person in a video are disclosed. Systems, methods, devices, and non-transitory computer readable media may include at least one processor that may be configured to generate a spatiotemporal emotion data compendium (STEM-DC) from the video and to process the STEM-DC using a deep fully adaptive graph convolutional network (FAGC) to determine a first person representation vector that represents the person in the video.

Abstract: The present disclosure generally relates to techniques for constructing a specialized artificial-intelligence (AI) architecture. The present disclosure relates to techniques for optimizing hyperparameters of the specialized AI architecture using reinforcement learning models, and generating a prediction of a user's behavior with respect to an obligation by executing the specialized AI architecture with the optimized hyperparameters. The specialized AI architecture can include a pre-processing layer that extracts features from unstructured user data and normalizes the features, a classifier layer that classifies users, and a normalization layer that normalizes the classifications of users.

Abstract: The present disclosure generally relates to techniques for constructing a specialized artificial-intelligence (AI) architecture. The present disclosure relates to techniques for optimizing hyperparameters of the specialized AI architecture using reinforcement learning models, and generating a prediction of a user's behavior with respect to an obligation by executing the specialized AI architecture with the optimized hyperparameters. The specialized AI architecture can include a pre-processing layer that extracts features from unstructured user data and normalizes the features, a classifier layer that classifies users, and a normalization layer that normalizes the classifications of users.

Abstract: Systems, methods, and computer readable media for identifying a person in a video are disclosed. Systems, methods, devices, and non-transitory computer readable media may include at least one processor that may be configured to generate a spatiotemporal emotion data compendium (STEM-DC) from the video and to process the STEM-DC using a deep fully adaptive graph convolutional network (FAGC) to determine a first person representation vector that represents the person in the video.

Abstract: A dynamic vision sensor (DVS) color camera (DVS-CCam) operable to acquire a color image of a scene, the DVS-CCam including a DVS photosensor comprising DVS pixels, an illuminator operable to transmit a light pattern characterized by temporal changes in intensity and color to illuminate a scene, an optical system configured to collect and focus on the pixels of the DVS photosensor light reflected by features in the scene from the light pattern transmitted by the illuminator, and a processor configured to process DVS signals generated by the DVS pixels responsive to temporal changes in the reflected light to provide a color image of the scene.

Abstract: The present disclosure generally relates to systems that include an artificial intelligence (AI) architecture for determining whether an image is manipulated. The architecture can include a constrained convolutional layer, separable convolutional layers, maximum-pooling layers, a global average-pooling layer, and a fully connected layer. In one specific example, the constrained convolutional layer can detect one or more image-manipulation fingerprints with respect to an image and can generate feature maps corresponding to the image. The global average-pooling layer can generate a vector of feature values by averaging the feature maps. The fully connected layer can then generate, based on the vector of feature values, an indication of whether the image was manipulated or not manipulated.

Abstract: A dynamic vision sensor (DVS) color camera (DVS-CCam) operable to acquire a color image of a scene, the DVS-CCam including a DVS photosensor comprising DVS pixels, an illuminator operable to transmit a light pattern characterized by temporal changes in intensity and color to illuminate a scene, an optical system configured to collect and focus on the pixels of the DVS photosensor light reflected by features in the scene from the light pattern transmitted by the illuminator, and a processor configured to process DVS signals generated by the DVS pixels responsive to temporal changes in the reflected light to provide a color image of the scene.

Abstract: The present disclosure generally relates to techniques for constructing an artificial-intelligence (AI) architecture. The present disclosure relates to techniques for executing the AI architecture to detect whether or not characters in a digital document have been manipulated. The AI architecture can be configured to classify each character in a digital document as manipulated or not manipulated by constructing a graph for each character, generating features for each node of the graph, and inputting a vector representation of the graph into a trained machine-learning model to generate the character classification.

Abstract: A computer-implemented method of extracting high-frequency features from data, including receiving a first dataset; in a training phase, applying frequency-based guidance to learnable high-frequency filters in a neural network, the frequency based-guidance including promoting high eigenvalues associated with eigenvectors comprising the learnable high-frequency filters; extracting from the first dataset high-frequency features associated with the eigenvectors; and using the trained high-frequency filters to extract high-frequency features from a second dataset.

Abstract: System and method for imaging a target, the method comprising transmitting a plurality of N pulses of electromagnetic waves to illuminate the target, receiving a pulse of electromagnetic waves that is reflected by the target from each of the transmitted pulses at an imager sensitive to the electromagnetic waves, integrating energy in the plurality of received pulses during a same exposure period of the imager to provide a measure of the integrated energy, and processing the measure of integrated energy to provide N images of the target.

Abstract: The present disclosure generally relates to techniques for constructing an artificial-intelligence (AI) architecture. The present disclosure relates to techniques for executing the AI architecture to detect whether or not characters in a digital document have been manipulated. The AI architecture can be configured to classify each character in a digital document as manipulated or not manipulated by constructing a graph for each character, generating features for each node of the graph, and inputting a vector representation of the graph into a trained machine-learning model to generate the character classification.

Abstract: The present disclosure generally relates to systems that include an artificial intelligence (AI) architecture for determining whether an image is manipulated. The architecture can include a constrained convolutional layer, separable convolutional layers, maximum-pooling layers, a global average-pooling layer, and a fully connected layer. In one specific example, the constrained convolutional layer can detect one or more image-manipulation fingerprints with respect to an image and can generate feature maps corresponding to the image. The global average-pooling layer can generate a vector of feature values by averaging the feature maps. The fully connected layer can then generate, based on the vector of feature values, an indication of whether the image was manipulated or not manipulated.

Abstract: The present disclosure generally relates to systems that include an artificial intelligence (AI) architecture for determining whether an image is manipulated. The architecture can include a constrained convolutional layer, separable convolutional layers, maximum-pooling layers, a global average-pooling layer, and a fully connected layer. In one specific example, the constrained convolutional layer can detect one or more image-manipulation fingerprints with respect to an image and can generate feature maps corresponding to the image. The global average-pooling layer can generate a vector of feature values by averaging the feature maps. The fully connected layer can then generate, based on the vector of feature values, an indication of whether the image was manipulated or not manipulated.

Abstract: The present disclosure generally relates to systems that include an artificial intelligence (AI) architecture for determining whether an image is manipulated. The architecture can include a constrained convolutional layer, separable convolutional layers, maximum-pooling layers, a global average-pooling layer, and a fully connected layer. In one specific example, the constrained convolutional layer can detect one or more image-manipulation fingerprints with respect to an image and can generate feature maps corresponding to the image. The global average-pooling layer can generate a vector of feature values by averaging the feature maps. The fully connected layer can then generate, based on the vector of feature values, an indication of whether the image was manipulated or not manipulated.

Abstract: The present disclosure generally relates to techniques for constructing an artificial-intelligence (AI) architecture. The present disclosure relates to techniques for executing the AI architecture to detect whether or not characters in a digital document have been manipulated. The AI architecture can be configured to classify each character in a digital document as manipulated or not manipulated by constructing a graph for each character, generating features for each node of the graph, and inputting a vector representation of the graph into a trained machine-learning model to generate the character classification.