Abstract: A system to reduce probability of a crash in an intersection is disclosed which includes one or more way-sensor systems, the one or more sensor systems adapted to provide position and velocity vector of an object approaching the intersection on an associated path, a processing unit including configured to: determine timing of the object with respect to a predefined zone of the intersection on the associated path, determine the current and future status of the associated traffic lights on the associated path of the object, predict timing of the object with respect to the predefined zone and future status of the associated traffic light, if timing prediction is within a predetermined threshold, modify the current status, wherein tgreen is extended, if timing prediction is outside of the predetermined threshold, modify the current status, wherein tgreen is reduced, and repeating above steps until the object has cleared the intersection.

Abstract: Approaches are described for determining facial landmarks in images. An input image is provided to at least one trained neural network that determines a face region (e.g., bounding box of a face) of the input image and initial facial landmark locations corresponding to the face region. The initial facial landmark locations are provided to a 3D face mapper that maps the initial facial landmark locations to a 3D face model. A set of facial landmark locations are determined from the 3D face model. The set of facial landmark locations are provided to a landmark location adjuster that adjusts positions of the set of facial landmark locations based on the input image. The input image is presented on a user device using the adjusted set of facial landmark locations.

Abstract: A system to reduce probability of a crash in an intersection is disclosed which includes one or more way-sensor systems, the one or more sensor systems adapted to provide position and velocity vector of an object approaching the intersection on an associated path, a processing unit including configured to: determine timing of the object with respect to a predefined zone of the intersection on the associated path, determine the current and future status of the associated traffic lights on the associated path of the object, predict timing of the object with respect to the predefined zone and future status of the associated traffic light, if timing prediction is within a predetermined threshold, modify the current status, wherein tgreen is extended, if timing prediction is outside of the predetermined threshold, modify the current status, wherein tgreen is reduced, and repeating above steps until the object has cleared the intersection.

Abstract: The training of a learning agent to provide real-time control of an object is disclosed. Training of the learning agent and training of a corresponding pioneer agent are iteratively alternated. The training of the learning and pioneer agents is under the supervision of a supervisor agent. The training of the learning agent provides feedback for subsequent training of the pioneer agent. The training of the pioneer agent provides feedback for subsequent training of the learning agent. During the training, a supervisor coefficient modulates the influence of the supervisor agent. As agents are trained, the influence of the supervisor agent is decayed. The training of the learning agent, under a first level of supervisor influence, includes real-time control of the object. The subsequent training of the pioneer agent, under a reduced level of supervisor influence, includes replay of training data accumulated during the real-time control of the object.

Abstract: A system to reduce probability of a crash in an intersection is disclosed which includes one or more way-sensor systems, the one or more sensor systems adapted to provide position and velocity vector of an object approaching the intersection on an associated path, a processing unit including configured to: determine position of the object with respect to a predefined zone of the intersection on the associated path, determine the current and future status of the associated traffic lights on the associated path of the object, predict position of the object with respect to the predefined zone and future status of the associated traffic light, if position prediction is within a predetermined threshold, modify the current status, wherein tgreen is extended, if position prediction is outside of the predetermined threshold, modify the current status, wherein tgreen is reduced, and repeating above steps until the object has cleared the intersection.

Abstract: A quantitative pulse count (event detection) algorithm with linearity to high count rates is accomplished by combining a high-speed, high frame rate camera with simple logic code run on a massively parallel processor such as a GPU or a FPGA. The parallel processor elements examine frames from the camera pixel by pixel to find and tag events or count pulses. The tagged events are combined to form a combined quantitative event image.

Abstract: The present invention provides a 19F-MR/fluorescence multi-mode molecular imaging and drug loading diagnosis-treatment integrated nanoprobe, and a preparation method and an application. The nano-probe is a nanoparticle formed by coating a mixture of a surfactant containing a molecular targeting treatment drug and a fluorescent dye with a Perfluorocarbon (PFC) carrier; and by uniformly dispersing a mixed solution into water and glycerol, processing ultrasonically, removing a component which is not effectively coated, and purifying, the drug-loading nanoparticle capable of being used for 19 F-MR imaging may be prepared.

Abstract: A system to reduce probability of a crash in an intersection is disclosed which includes one or more way-sensor systems, the one or more sensor systems adapted to provide position and velocity vector of an object approaching the intersection on an associated path, a processing unit including configured to: determine position of the object with respect to a predefined zone of the intersection on the associated path, determine the current and future status of the associated traffic lights on the associated path of the object, predict position of the object with respect to the predefined zone and future status of the associated traffic light, if position prediction is within a predetermined threshold, modify the current status, wherein tgreen is extended, if position prediction is outside of the predetermined threshold, modify the current status, wherein tgreen is reduced, and repeating above steps until the object has cleared the intersection.

Abstract: A quantitative pulse count (event detection) algorithm with linearity to high count rates is accomplished by combining a high-speed, high frame rate camera with simple logic code run on a massively parallel processor such as a GPU or a FPGA. The parallel processor elements examine frames from the camera pixel by pixel to find and tag events or count pulses. The tagged events are combined to form a combined quantitative event image.

Abstract: A quantitative pulse count (event detection) algorithm with linearity to high count rates is accomplished by combining a high-speed, high frame rate camera with simple logic code run on a massively parallel processor such as a GPU. The parallel processor elements examine frames from the camera pixel by pixel to find and tag events or count pulses. The tagged events are combined to form a combined quantitative event image.

Abstract: Disclosed herein are embodiments of systems, methods, and products for a webly supervised training of a convolutional neural network (CNN) to predict emotion in images. A computer may query one or more image repositories using search keywords generated based on the tertiary emotion classes of Parrott's emotion wheel. The computer may filter images received in response to the query to generate a weakly labeled training dataset labels associated with the images that are noisy or wrong may be cleaned prior to training of the CNN. The computer may iteratively train the CNN leveraging the hierarchy of emotion classes by increasing the complexity of the labels (tags) for each iteration. Such curriculum guided training may generate a trained CNN that is more accurate than the conventionally trained neural networks.

Abstract: Methods and systems are provided for generating a facial recognition system. A facial recognition system can be implemented using a meta-model based on a trained neural network. A neural network can be trained as multiple classifiers that identify individuals using a small number of images of the individual's face. A meta-model can learn from the neural networks to be capable to identify an individual based on a small number of images. In this way, the facial recognition system uses the meta-model that learns from the neural network trained to identify an individual based on a small number of images. Such a facial recognition system is tested to determine any misidentification for fine-tuning the system. A facial recognition system implemented using such a meta-model is capable of adapting the model to learn identities entered into the system using only a small number of images to enroll an identity into the system.

Abstract: The present invention provides a 19F-MR/fluorescence multi-mode molecular imaging and drug loading diagnosis-treatment integrated nanoprobe, and a preparation method and an application. The nano-probe is a nanoparticle formed by coating a mixture of a surfactant containing a molecular targeting treatment drug and a fluorescent dye with a Perfluorocarbon (PFC) carrier; and by uniformly dispersing a mixed solution into water and glycerol, processing ultrasonically, removing a component which is not effectively coated, and purifying, the drug-loading nanoparticle capable of being used for 19 F-MR imaging may be prepared.

Abstract: Methods and systems are provided for generating a facial recognition system. A facial recognition system can be implemented using a meta-model based on a trained neural network. A neural network can be trained as multiple classifiers that identify individuals using a small number of images of the individual's face. A meta-model can learn from the neural networks to be capable to identify an individual based on a small number of images. In this way, the facial recognition system uses the meta-model that learns from the neural network trained to identify an individual based on a small number of images. Such a facial recognition system is tested to determine any misidentification for fine-tuning the system. A facial recognition system implemented using such a meta-model is capable of adapting the model to learn identities entered into the system using only a small number of images to enroll an identity into the system.

Abstract: A quantitative pulse count (event detection) algorithm with linearity to high count rates is accomplished by combining a high-speed, high frame rate camera with simple logic code run on a massively parallel processor such as a GPU. The parallel processor elements examine frames from the camera pixel by pixel to find and tag events or count pulses. The tagged events are combined to form a combined quantitative event image.

Abstract: Methods and systems for recognizing people in images with increased accuracy are disclosed. In particular, the methods and systems divide images into a plurality of clusters based on common characteristics of the images. The methods and systems also determine an image cluster to which an image with an unknown person instance most corresponds. One or more embodiments determine a probability that the unknown person instance is each known person instance in the image cluster using a trained cluster classifier of the image cluster. Optionally, the methods and systems determine context weights for each combination of an unknown person instance and each known person instance using a conditional random field algorithm based on a plurality of context cues associated with the unknown person instance and the known person instances. The methods and systems calculate a contextual probability based on the cluster-based probabilities and context weights to identify the unknown person instance.

Abstract: This disclosure covers methods, non-transitory computer readable media, and systems that learn attribute attention projections for attributes of digital images and parameters for an attention controlled neural network. By iteratively generating and comparing attribute-modulated-feature vectors from digital images, the methods, non-transitory computer readable media, and systems update attribute attention projections and parameters indicating either one (or both) of a correlation between some attributes of digital images and a discorrelation between other attributes of digital images. In certain embodiments, the methods, non-transitory computer readable media, and systems use the attribute attention projections in an attention controlled neural network as part of performing one or more tasks.

Abstract: The training of a learning agent to provide real-time control of an object is disclosed. Training of the learning agent and training of a corresponding pioneer agent are iteratively alternated. The training of the learning and pioneer agents is under the supervision of a supervisor agent. The training of the learning agent provides feedback for subsequent training of the pioneer agent. The training of the pioneer agent provides feedback for subsequent training of the learning agent. During the training, a supervisor coefficient modulates the influence of the supervisor agent. As agents are trained, the influence of the supervisor agent is decayed. The training of the learning agent, under a first level of supervisor influence, includes real-time control of the object. The subsequent training of the pioneer agent, under a reduced level of supervisor influence, includes replay of training data accumulated during the real-time control of the object.

Abstract: Approaches are described for determining facial landmarks in images. An input image is provided to at least one trained neural network that determines a face region (e.g., bounding box of a face) of the input image and initial facial landmark locations corresponding to the face region. The initial facial landmark locations are provided to a 3D face mapper that maps the initial facial landmark locations to a 3D face model. A set of facial landmark locations are determined from the 3D face model. The set of facial landmark locations are provided to a landmark location adjuster that adjusts positions of the set of facial landmark locations based on the input image. The input image is presented on a user device using the adjusted set of facial landmark locations.

Abstract: Methods and systems for recognizing people in images with increased accuracy are disclosed. In particular, the methods and systems divide images into a plurality of clusters based on common characteristics of the images. The methods and systems also determine an image cluster to which an image with an unknown person instance most corresponds. One or more embodiments determine a probability that the unknown person instance is each known person instance in the image cluster using a trained cluster classifier of the image cluster. Optionally, the methods and systems determine context weights for each combination of an unknown person instance and each known person instance using a conditional random field algorithm based on a plurality of context cues associated with the unknown person instance and the known person instances. The methods and systems calculate a contextual probability based on the cluster-based probabilities and context weights to identify the unknown person instance.