Abstract: Automatic central processing unit (CPU) usage optimization includes: monitoring performance activity of a workload comprising a plurality of threads; and modifying a resource allocation of a plurality of cores for the plurality of threads based on the performance activity.

Abstract: A processing unit of a processing system compiles a priority queue listing of a plurality of processor cores to run a workload based on a cost of running the workload on each of the processor cores. The cost is based on at least one of a system usage policy, characteristics of the workload, and one or more physical constraints of each processor core. The processing unit selects a processor core based on the cost to run the workload and communicates an identifier of the selected processor core to an operating system of the processing system.

Abstract: High throughput cell free protein synthesis methods and systems are provided for expression of active, mature enzymes and proteins derived from zymogens and proproteins. Zymogens and proproteins are expressed in their mature, active form in a cell free expression system, without cleavage of a pro-sequence to produce the active polypeptide.

Abstract: Implementations of a trailer with isolated suites are provided. In some implementations, the trailer with isolated suites comprises a road transportable trailer with a plurality of separate isolated living suites or units each having a separate independent heating and air conditioning system and an upper bed or a lower bed alternating on each side of the trailer such that the beds of side-adjacent units are positioned in a vertical bunk bed configuration with the beds separately isolated within in each unit.

Abstract: A processing unit of a processing system compiles a priority queue listing of a plurality of processor cores to run a workload based on a cost of running the workload on each of the processor cores. The cost is based on at least one of a system usage policy, characteristics of the workload, and one or more physical constraints of each processor core. The processing unit selects a processor core based on the cost to run the workload and communicates an identifier of the selected processor core to an operating system of the processing system.

Abstract: Systems and methods are disclosed for modifying labeled target content for a capture device. A computer-implemented method may use a computer system that includes non-transient electronic storage, a graphical user interface, and one or more physical computer processors. The computer-implemented method may include: obtaining labeled target content, the labeled target content including one or more facial features that have been labeled; modifying the labeled target content to match dynamically captured content from a first capture device to generate modified target content; and storing the modified target content. The dynamically captured content may include the one or more facial features.

Abstract: Automatic central processing unit (CPU) usage optimization includes: monitoring performance activity of a workload comprising a plurality of threads; and modifying a resource allocation of a plurality of cores for the plurality of threads based on the performance activity.

Abstract: Techniques for selectively removing Monte Carlo (MC) noise from a geometric buffer (G-buffer). Embodiments identify the G-buffer for rendering an image of a three-dimensional scene from a viewpoint. Embodiments determine, for each of a plurality of pixels in the image being rendered, respective world position information based on the three-dimensional scene and a position and orientation of the viewpoint. A pre-filtering operation is then performed to selectively remove the MC noise from the G-buffer, based on the determined world position information for the plurality of pixels.

Abstract: Presented herein are systems and methods configured to generate virtual entities representing real-world users. In some implementations, the systems and/or methods are configured to capture user appearance information with imaging devices and sensors, determines correspondence values conveying correspondences between the appearance of the user's body or user's head and individual ones of default body models and/or default head models, modifies a set of values defining a base body model and/or base head model based on determined correspondence values and sets of base values defining the default body models and/or default head models. The base body model and/or base head model may be modified to model the appearance of the body and/or head of the user.

Abstract: Systems and methods are disclosed for modifying labeled target content for a capture device. A computer-implemented method may use a computer system that includes non-transient electronic storage, a graphical user interface, and one or more physical computer processors. The computer-implemented method may include: obtaining labeled target content, the labeled target content including one or more facial features that have been labeled; modifying the labeled target content to match dynamically captured content from a first capture device to generate modified target content; and storing the modified target content. The dynamically captured content may include the one or more facial features.

Abstract: Techniques for selectively removing Monte Carlo (MC) noise from a geometric buffer (G-buffer). Embodiments identify the G-buffer for rendering an image of a three-dimensional scene from a viewpoint. Embodiments determine, for each of a plurality of pixels in the image being rendered, respective world position information based on the three-dimensional scene and a position and orientation of the viewpoint. A pre-filtering operation is then performed to selectively remove the MC noise from the G-buffer, based on the determined world position information for the plurality of pixels.

Abstract: Presented herein are systems and methods configured to generate virtual entities representing real-world users. In some implementations, the systems and/or methods are configured to capture user appearance information with imaging devices and sensors, determines correspondence values conveying correspondences between the appearance of the user's body or user's head and individual ones of default body models and/or default head models, modifies a set of values defining a base body model and/or base head model based on determined correspondence values and sets of base values defining the default body models and/or default head models. The base body model and/or base head model may be modified to model the appearance of the body and/or head of the user.

Abstract: Techniques for selectively removing Monte Carlo (MC) noise from a geometric buffer (G-buffer). Embodiments identify the G-buffer for rendering an image of a three-dimensional scene from a viewpoint. Embodiments determine, for each of a plurality of pixels in the image being rendered, respective world position information based on the three-dimensional scene and a position and orientation of the viewpoint. A pre-filtering operation is then performed to selectively remove the MC noise from the G-buffer, based on the determined world position information for the plurality of pixels.

Abstract: Presented herein are systems and methods configured to generate virtual entities representing real-world users. In some implementations, the systems and/or methods are configured to capture user appearance information with imaging devices and sensors, determines correspondence values conveying correspondences between the appearance of the user's body or user's head and individual ones of default body models and/or default head models, modifies a set of values defining a base body model and/or base head model based on determined correspondence values and sets of base values defining the default body models and/or default head models. The base body model and/or base head model may be modified to model the appearance of the body and/or head of the user.

Abstract: Presented herein are systems and methods configured to generate virtual entities representing real-world users. In some implementations, the systems and/or methods are configured to capture user appearance information with imaging devices and sensors, determines correspondence values conveying correspondences between the appearance of the user's body or user's head and individual ones of default body models and/or default head models, modifies a set of values defining a base body model and/or base head model based on determined correspondence values and sets of base values defining the default body models and/or default head models. The base body model and/or base head model may be modified to model the appearance of the body and/or head of the user.

Abstract: Provided are methods, systems, and computer-readable medium for synthetically generating training data to be used to train a learning algorithm that is capable of generating computer-generated images of a subject from real images that include the subject. The training data can be generated using a facial rig by changing expressions, camera viewpoints, and illumination in the training data. The training data can then be used for tracking faces in a real-time video stream. In such examples, the training data can be tuned to expected environmental conditions and camera properties of the real-time video stream. Provided herein are also strategies to improve training set construction by analyzing which attributes of a computer-generated image (e.g., expression, viewpoint, and illumination) require denser sampling.

Abstract: Techniques for selectively removing Monte Carlo (MC) noise from a geometric buffer (G-buffer). Embodiments identify the G-buffer for rendering an image of a three-dimensional scene from a viewpoint. Embodiments determine, for each of a plurality of pixels in the image being rendered, respective world position information based on the three-dimensional scene and a position and orientation of the viewpoint. A pre-filtering operation is then performed to selectively remove the MC noise from the G-buffer, based on the determined world position information for the plurality of pixels.

Abstract: Techniques for displaying an augmented reality environment. Embodiments capture a visual scene for display. An embedded symbol associated with a garment worn by the first user within the visual scene is identified. Embodiments generate a virtual augmentation corresponding to the first user that is animated based on the captured visual scene. The virtual augmentation alters an appearance of the first user, such that the first user is depicted as wearing a virtual costume, the virtual costume determined to have a predefined relationship with the identified embedded symbol. A sequence of frames is rendered that depicts the first user augmented with the virtual augmentation in the augmented reality environment and depict at least a portion of the first user as wearing the virtual costume.

Abstract: Systems, methods and articles of manufacture for rendering an image. Embodiments include selecting a plurality of positions within the image and constructing a respective linear prediction model for each selected position. A respective prediction window is determined for each constructed linear prediction model. Additionally, embodiments render the image using the linear prediction models using the constructed linear prediction models, where at least one of the constructed linear prediction models is used to predict values for two or more of a plurality of pixels of the image, and where a value for at least one of the plurality of pixels is determined based on two or more of the constructed linear prediction models.

Abstract: Systems, methods and articles of manufacture for rendering an image. Embodiments include selecting a plurality of positions within the image and constructing a respective linear prediction model for each selected position. A respective prediction window is determined for each constructed linear prediction model. Additionally, embodiments render the image using the linear prediction models using the constructed linear prediction models, where at least one of the constructed linear prediction models is used to predict values for two or more of a plurality of pixels of the image, and where a value for at least one of the plurality of pixels is determined based on two or more of the constructed linear prediction models.