Abstract: In various embodiments, a map inference application automatically maps a user space. A camera is positioned within the user space. In operation, the map inference application determines a path of a first moving object within the user space based on a tracking dataset generated from images captured by the camera. Subsequently, the map inference application infers a walking space within the user space based on the path. The map inference application then generates a model of at least a portion of the user space based on the walking space. One or more movements of a second object within the user space are based on the model. Advantageously, unlike prior art solutions, the map inference application enables a model of a user space to be automatically and efficiently generated based on images from a single stationary camera.

Abstract: In one implementation, a virtual-world simulator includes a computing platform having a hardware processor and a memory storing a software code, a tracking system communicatively coupled to the computing platform, and a projection device communicatively coupled to the computing platform. The hardware processor is configured to execute the software code to obtain a map of a geometry of a real-world venue including the virtual-world simulator, to identify one or more virtual effects for display in the real-world venue, and to use the tracking system to track a moving perspective of one of a user in the real-world venue or a camera in the real-world venue. The hardware processor is further configured to execute the software code to control the projection device to simulate a virtual-world by conforming the identified one or more virtual effects to the geometry of the real-world venue from a present vantage point of the tracked moving perspective.

Abstract: A modeling engine generates a prediction model that quantifies and predicts secondary dynamics associated with the face of a performer enacting a performance. The modeling engine generates a set of geometric representations that represents the face of the performer enacting different facial expressions under a range of loading conditions. For a given facial expression and specific loading condition, the modeling engine trains a Machine Learning model to predict how soft tissue regions of the face of the performer change in response to external forces applied to the performer during the performance. The modeling engine combines different expression models associated with different facial expressions to generate a prediction model. The prediction model can be used to predict and remove secondary dynamics from a given geometric representation of a performance or to generate and add secondary dynamics to a given geometric representation of a performance.

Abstract: Some implementations of the disclosure are directed to a method, comprising: receiving tapped data that was tapped from a controller of an industrial control system (ICS) while the controller executed first control code to control ICS devices of the ICS, the tapped data used during execution of the first control code, and the tapped data comprising input data obtained from one or more input components of the controller communicatively coupled to the ICS devices, or output data obtained from one or more output components of the controller communicatively coupled to the ICS devices; and after receiving the tapped data, executing, using at least the tapped data, second control code that provides an emulation of the controller, the emulation comprising running, using at least the tapped data, a process of the first control code at a faster rate than it is run by the controller executing the first control code.

Abstract: Methods and apparatuses are provided, which may be implemented to combine and distribute user enhanced video and/or audio content.

Abstract: The present disclosure describes a method for displaying supplemental content. The method includes determining environmental characteristics, e.g., wall space, empty areas, colors, etc., for a display environment, determining supplemental content based in part on a primary content displayed by a primary display, and displaying the supplemental content in the display environment.

Abstract: Techniques are disclosed for changing the identities of faces in images. In embodiments, a tunable model for changing facial identities in images includes an encoder, a decoder, and dense layers that generate either adaptive instance normalization (AdaIN) coefficients that control the operation of convolution layers in the decoder or the values of weights within such convolution layers, allowing the model to change the identity of a face in an image based on a user selection. A separate set of dense layers may be trained to generate AdaIN coefficients for each of a number of facial identities, and the AdaIN coefficients output by different sets of dense layers can be combined to interpolate between facial identities. Alternatively, a single set of dense layers may be trained to take as input an identity vector and output AdaIN coefficients or values of weighs within convolution layers of the decoder.

Abstract: One embodiment of the present invention sets forth a technique for performing deinterlacing. The technique includes separating a first interlaced video frame into a first sequence of fields ordered by time, the first sequence of fields including a first field. The technique also includes generating, by applying a deinterlacing network to a first field in the first sequence, a second field that is missing from the first sequence of fields and is complementary to the first field. The technique further includes constructing a progressive video frame based on the first field and the second field.

Abstract: In various embodiments, a relevance application quantifies how a trained neural network operates. In operation, the relevance application generates a set of input distributions based on a set of input points associated with the trained neural network. Each input distribution is characterized by a mean and a variance associated with a different neuron included in the trained neural network. The relevance application propagates the set of input distributions through a probabilistic neural network to generate at least a first output distribution. The probabilistic neural network is derived from at least a portion of the trained neural network. Based on the first output distribution, the relevance application computes a contribution of a first input point included in the set of input points to a difference between a first output point associated with a first output of the trained neural network and an estimated mean prediction associated with the first output.

Abstract: Systems and methods are described for leveraging machine learning to deliver time-sensitive media content offers to a consumer over an optimal device and channel. In an implementation, a server system transmits media content offers over an optimal device and channel to a user. The server system captures metadata of at least one of: the user's use of multiple devices, the user's media content preferences, the user's preferred transmission channels for receiving media content offers over the multiple devices, and the user's preferred times for receiving media content offers. The server system prepares a media content offer for transmission to the user. Based on the captured metadata, the server system selects a user device of the multiple user devices and a transmission channel of multiple transmission channels to transmit the media content offer; and transmits the media content offer to the selected user device over the selected transmission channel.

Abstract: Techniques relating to caching a content object are disclosed. A first server receives a revalidation request, relating to a content object, from a requestor. It is determined that metadata, relating to the content object, is stored in a cache associated with the first server. In response, the first server determines not to retrieve the content object from a second server. The content object is not stored in the cache. A response to the revalidation request, indicating that the content object is not modified, is transmitted from the first server to the requestor, based on analyzing the metadata.

Abstract: Some implementations of the disclosure are directed to tapping input/output (I/O) data from an industrial control system (ICS) or applying the tapped I/O data to a learned model to perform predictive or prescriptive maintenance. In one implementation, a method comprises: tapping I/O data from a controller of an ICS while the controller executes first control code to control one or more devices of the ICS; transmitting the tapped I/O data over a network to a second system; and executing, via the second system, second control code comprising an original or modified version of all or a subset of the first control code of the ICS, wherein the second control code executes in response to receiving the tapped I/O data. The output of executing the second control code may be provided to a model to predict a future event involving the ICS or to prescribe maintenance of the ICS.

Abstract: A method, comprises monitoring a encoding process of a source video file performed by an encoder; obtaining an encoding decision parameter used to encode a picture of the source video file during the encoding process; comparing the encoding decision parameter to a threshold; based on the step of comparing, identifying the picture as a candidate picture for a visual defect or coding error; and storing a timestamp of the candidate picture.

Abstract: According to one implementation, a system for performing graph-based media content evaluation includes a computing platform having a hardware processor, and a system memory storing a media content evaluation software code and a graph database. The hardware processor is configured to execute the media content evaluation software code to receive a query from a system user, and to identify one or more media content evaluation metrics corresponding to the query. In addition, the hardware processor is configured to execute the media content evaluation software code to search the graph database for a media content data relevant to the one or more media content evaluation metrics, and to retrieve the media content data from the graph database. The hardware processor is further configured to execute the media content evaluation software code to generate a report responsive to the query using the media content data.

Abstract: A method includes producing a filter mask based on a blur mask and a saliency mask and identifying locations of a plurality of bounding boxes of a plurality of objects of interest in a received image. The method also includes applying the filter mask to the received image and to the locations of the plurality of bounding boxes in the received image to remove at least one object of interest from consideration. The method further includes performing a comparison of a location of a bounding box of the plurality of bounding boxes of an object of interest remaining in consideration to a predetermined safe region of the received image and generating a validation result based on the comparison.

Abstract: An electronic device that includes one or more input sensor devices, one or more output devices, one or more computer processors and a memory containing computer program code that, when executed by operation of the one or more computer processors, performs an operation. The operation includes collecting information, using one or more input sensor devices, about the plurality of users within a physical environment. The operation includes analyzing the collected information to determine a present situational context for the plurality of users that are currently present within the physical environment. An action to perform is determined based on the determined present situational context. The determined action is executed using the one or more output devices.

Abstract: Systems, methods and articles of manufacture for are provided for analyzing user behavior in real time by ingesting telemetry data related to a streaming media application; feeding the telemetry data to a machine learning model (MLM) that produces a User Experience (UX) command based on the telemetry data and prior telemetry data received from the content streaming application; selecting content items to provide to the client device based on the telemetry data; determining, based on the telemetry data, whether the client device has sufficient free resources to receive the UX command and the content items in a current time window while providing a predefined level of service; when client device has sufficient free resources to receive the UX command and the content items, encapsulating the UX command with the content items in a content stream; and transmitting the content stream to the client device.

Abstract: Systems and methods for compressing target content are disclosed. In one embodiment, a system may include non-transient electronic storage and one or more physical computer processors. The one or more physical computer processors may be configured by machine-readable instructions to obtain the target content comprising one or more frames, wherein a given frame comprises one or more features. The one or more physical computer processors may be configured by machine-readable instructions to obtain a conditioned network. The one or more physical computer processors may be configured by machine-readable instructions to generate decoded target content by applying the conditioned network to the target content.

Abstract: A method for use by a system computer for granting access to a network resource includes receiving, in a first session, a first request for accessing the network resource, the first request including authorization information of a user of an access computer, initiating, while in the first session and using the first authorization information, a first authorization process with one or more authorization computers, and determining a failure relating to the first authorization process initiated with the one or more authorization computers. The method further includes, upon determining the failure, sending a second request indicating a temporary access token is to be delivered to the access computer in response to an access computer executing a session refresh for initiating a second session.