Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.

Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.

Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.

Abstract: The present disclosure relates to using a neural network to efficiently denoise images that were generated by a ray tracer. The neural network can be trained using noisy images generated with noisy samples and corresponding denoised or high-sampled images (e.g., many random samples). An input feature to the neural network can include color from pixels of an image. Other input features to the neural network, which would not be known in normal image processing, can include shading normal, depth, albedo, and other characteristics available from a computer-generated scene. After the neural network is trained, a noisy image that the neural network has not seen before can have noise removed without needing manual intervention.

Abstract: Example apparatus and methods for rigging a torque tube assembly in an aircraft are described herein. An example torque tube assembly includes a torque tube and a spline coupling coupled to an end of the torque tube. The spline coupling has an opening to receive a spline gear on a drive shaft of an aircraft high lift device. The torque tube assembly also includes a retainer coupled to the spline coupling. The retainer blocks at least a portion of the opening in the spline coupling to prevent the spline coupling from being moved off of the spline gear on the drive shaft.

Abstract: A telematics control system includes: an external server configured to serialize a script with an external protocol buffer and transmit the serialized script to a telematics control units (TCU); a vehicle having sensors and the TCU, the TCU configured to: deserialize the script with a TCU protocol buffer, execute the script via an interpreter preloaded on the TCU, store data from the sensors based on the script.

Abstract: The present disclosure relates to using a neural network to efficiently denoise images that were generated by a ray tracer. The neural network can be trained using noisy images generated with noisy samples and corresponding denoised or high-sampled images (e.g., many random samples). An input feature to the neural network can include color from pixels of an image. Other input features to the neural network, which would not be known in normal image processing, can include shading normal, depth, albedo, and other characteristics available from a computer-generated scene. After the neural network is trained, a noisy image that the neural network has not seen before can have noise removed without needing manual intervention.

Abstract: An internal combustion engine includes an engine block, an electric motor, a worm coupled to and rotated by the electric motor, a worm gear coupled to and configured to be rotated by the worm, a crankshaft configured to rotate about a crankshaft axis, a flywheel including a flywheel cup having flywheel protrusions, a clutch driven by the worm gear and configured to engage the one or more flywheel protrusions such that the crankshaft is rotated, and an energy storage device electrically coupled to the electric motor. The clutch is configured to disengage from the flywheel protrusions when the rotational speed of the crankshaft exceeds the rotational speed of the worm gear. When the electric motor is activated, the electric motor rotates the worm rotating the worm gear, which causes the clutch to engage the flywheel protrusions, transferring worm gear rotation to the flywheel and the crankshaft to rotate the engine for starting.

Abstract: A secured medicines dispensing device for controlling access to medications includes a housing. A plurality of slats that is coupled to and extends between an annular wall of the housing to define a plurality of compartments. Each of a plurality of lids is positioned in a top of the housing and biased to an open configuration. A power module, a microprocessor and transmitter are coupled to the housing. A screen, which is touch-enabled, and a scanner configured to read a fingerprint of a user are coupled to the top of the housing. The microprocessor is operationally coupled to the power module. The transmitter, the screen, and the scanner are operationally coupled to the microprocessor. A plurality of locks is operationally coupled to the microprocessor. Each lock is coupled to a respective lid and selectively couplable to the housing to secure the lid in a closed configuration.

Abstract: This disclosure provides an approach for automatically generating UV maps for modified three-dimensional (3D) virtual geometry. In one embodiment, a UV generating application may receive original 3D geometry and associated UV panels, as well as modified 3D geometry created by deforming the original 3D geometry. The UV generating application then extracts principal stretches of a mapping between the original 3D geometry and the associated UV panels and transfers the principal stretches, or a function thereof, to a new UV mapping for the modified 3D geometry. Transferring the principal stretches or the function thereof may include iteratively performing the following steps: determining new UV points assuming a fixed affine transformation, determining principal stretches of a transformation between the modified 3D geometry and the determined UV points, and determining a correction of a transformation matrix for each triangle to make the matrix a root of a scoring function.

Abstract: A system for peer to peer collaboration and learning through a mobile application is provided. The system includes a remote network hosting a database of institution specific information including a list of available classes. A mobile application is operable to connect remotely to the remote network. A profile feature is provided in the mobile application and is customizable to a user of the application to allow the user to input information. The mobile application includes a menu feature allowing access to the list of available classes and allows the user to select a class from a list of classes. A classmate feature is provided in the application to provide a list of classmates enrolled in a corresponding class. A study group feature is provided to allow the user to generate a study group having one or more parameters. The mobile application further includes a map feature.

Abstract: Supervised machine learning using neural networks is applied to denoising images rendered by MC path tracing. Specialization of neural networks may be achieved by using a modular design that allows reusing trained components in different networks and facilitates easy debugging and incremental building of complex structures. Specialization may also be achieved by using progressive neural networks. In some embodiments, training of a neural-network based denoiser may use importance sampling, where more challenging patches or patches including areas of particular interests within a training dataset are selected with higher probabilities than others. In some other embodiments, generative adversarial networks (GANs) may be used for training a machine-learning based denoiser as an alternative to using pre-defined loss functions.

Abstract: Supervised machine learning using neural networks is applied to denoising images rendered by MC path tracing. Specialization of neural networks may be achieved by using a modular design that allows reusing trained components in different networks and facilitates easy debugging and incremental building of complex structures. Specialization may also be achieved by using progressive neural networks. In some embodiments, training of a neural-network based denoiser may use importance sampling, where more challenging patches or patches including areas of particular interests within a training dataset are selected with higher probabilities than others. In some other embodiments, generative adversarial networks (GANs) may be used for training a machine-learning based denoiser as an alternative to using pre-defined loss functions.

Abstract: Supervised machine learning using neural networks is applied to denoising images rendered by MC path tracing. Specialization of neural networks may be achieved by using a modular design that allows reusing trained components in different networks and facilitates easy debugging and incremental building of complex structures. Specialization may also be achieved by using progressive neural networks. In some embodiments, training of a neural-network based denoiser may use importance sampling, where more challenging patches or patches including areas of particular interests within a training dataset are selected with higher probabilities than others. In some other embodiments, generative adversarial networks (GANs) may be used for training a machine-learning based denoiser as an alternative to using pre-defined loss functions.

Abstract: The present disclosure relates to using a neural network to efficiently denoise images that were generated by a ray tracer. The neural network can be trained using noisy images generated with noisy samples and corresponding denoised or high-sampled images (e.g., many random samples). An input feature to the neural network can include color from pixels of an image. Other input features to the neural network, which would not be known in normal image processing, can include shading normal, depth, albedo, and other characteristics available from a computer-generated scene. After the neural network is trained, a noisy image that the neural network has not seen before can have noise removed without needing manual intervention.

Abstract: A secured medicines dispensing device for controlling access to medications includes a housing. A plurality of slats that is coupled to and extends between an annular wall of the housing to define a plurality of compartments. Each of a plurality of lids is positioned in a top of the housing and biased to an open configuration. A power module, a microprocessor and transmitter are coupled to the housing. A screen, which is touch-enabled, and a scanner configured to read a fingerprint of a user are coupled to the top of the housing. The microprocessor is operationally coupled to the power module. The transmitter, the screen, and the scanner are operationally coupled to the microprocessor. A plurality of locks is operationally coupled to the microprocessor. Each lock is coupled to a respective lid and selectively couplable to the housing to secure the lid in a closed configuration.

Abstract: A medical visualization platform including a base unit that includes a base unit connection mechanism, a processor, an electrical contact, a communication module, and a power source. A plurality of visualization attachments connect lo the base unit, and each visualization attachment is disposable and includes a visualization connection mechanism arranged to engage the base unit connection mechanism to provide movement of the connected visualization attachment relative to the base unit between a folded position and an engaged position. Each visualization attachment also includes attachment contacts that are in electrical communication with the electrical contact of the base unit while the visualization attachment is in the engaged position, and each of the visualization attachments includes either a video camera or a light source.

Abstract: A tube seal assembly is provided to connect a tube and an adapter in order to circulate a fluid. The tube seal assembly can include various components such as a locking flange, a tube retainer, a seal guard, and an elastomeric seal. Bolts are used to secure the components of tube seal assembly together through the use of a load and to secure the tube seal assembly to the second tube. Teeth on the tube retainer are angled to dig into the first tube to prevent the tube from separating from the adapter during a blowout. The tube seal assembly can be configured to connect to any existing tube in order to be more serviceable in the field.

Abstract: This disclosure provides an approach for automatically generating UV maps for modified three-dimensional (3D) virtual geometry. In one embodiment, a UV generating application may receive original 3D geometry and associated UV panels, as well as modified 3D geometry created by deforming the original 3D geometry. The UV generating application then extracts principle stretches of a mapping between the original 3D geometry and the associated UV panels and transfers the principle stretches, or a function thereof, to a new UV mapping for the modified 3D geometry. Transferring the principle stretches or the function thereof may include iteratively performing the following steps: determining new UV points assuming a fixed affine transformation, determining principle stretches of a transformation between the modified 3D geometry and the determined UV points, and determining a correction of a transformation matrix for each triangle to make the matrix a root of a scoring function.

Abstract: A telematics control system includes: an external server configured to serialize a script with an external protocol buffer and transmit the serialized script to a TCU; a vehicle having sensors and the TCU, the TCU configured to: deserialize the script with a TCU protocol buffer, execute the script via an interpreter preloaded on the TCU, store data from the sensors based on the script.