Abstract: An internal combustion engine includes an engine block, a blower housing configured to direct cooling air to the engine block, an electric starting system, and a crankshaft configured to rotate about a crankshaft axis. The electric starting system includes an electric motor and an energy storage device located within the blower housing. The energy storage device is electrically coupled to the electric motor to power the electric motor. When the starter motor is activated, the electric starting system rotates the crankshaft to rotate the engine for starting.

Abstract: A 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: 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: Embodiments of the present invention are directed to methods and systems for treating irrigation water by introducing a propagating electromagnetic field into the irrigation water as it flows through an irrigation system. The treatments described herein may have a variety of beneficial effects on the water, including a significant increase in the percentage of the water that is maintained in the root zone of a given crop as plant-available water and the essential mineral, e.g. calcium and/or magnesium, uptake of that crop.

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: Embodiments of the present invention are directed to methods and systems for treating irrigation water by introducing a propagating electromagnetic field into the irrigation water as it flows through an irrigation system. The treatments described herein may have a variety of beneficial effects on the water, including a significant increase in the percentage of the water that is maintained in the root zone of a given crop as plant-available water and the essential mineral, e.g. calcium and/or magnesium, uptake of that crop.

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: 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 retention device for interconnecting a lash adjuster and a finger follower that supports a bearing of a valve actuating mechanism for an internal combustion engine, wherein the retention device includes a body having a lower member, an upper member spaced from the lower member, and an intermediate member interconnecting the lower and upper members. The lower member includes an aperture that is adapted to be received in a groove of the lash adjuster. The intermediate member is secured to the finger follower such that the retention device interconnects the lash adjuster and the finger follower. The upper member includes a bearing retention mechanism that limits movement of the bearing of the finger follower and retains the bearing relative to the finger follower prior to mounting the finger follower and lash adjuster as a part of the valve actuating mechanism of the internal combustion engine.

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: 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: 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: 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: Embodiments of the present invention are directed to methods and systems for treating irrigation water by introducing a propagating electromagnetic field into the irrigation water as it flows through an irrigation system. The treatments described herein may have a variety of beneficial effects on the water, including a significant increase in the percentage of the water that is maintained in the root zone of a given crop as plant-available water and the essential mineral, e.g. calcium and/or magnesium, uptake of that crop.

Abstract: An internal combustion engine includes an engine block, a blower housing configured to direct cooling air to the engine block, an electric starting system, and a crankshaft configured to rotate about a crankshaft axis. The electric starting system includes an electric motor and an energy storage device located within the blower housing. The energy storage device is electrically coupled to the electric motor to power the electric motor. When the starter motor is activated, the electric starting system rotates the crankshaft to rotate the engine for starting.

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: A retention device for interconnecting a lash adjuster and a finger follower that supports a bearing of a valve actuating mechanism for an internal combustion engine, wherein the retention device includes a body having a lower member, an upper member spaced from the lower member, and an intermediate member interconnecting the lower and upper members. The lower member includes an aperture that is adapted to be received in a groove of the lash adjuster. The intermediate member is secured to the finger follower such that the retention device interconnects the lash adjuster and the finger follower. The upper member includes a bearing retention mechanism that limits movement of the bearing of the finger follower and retains the bearing relative to the finger follower prior to mounting the finger follower and lash adjuster as a part of the valve actuating mechanism of the internal combustion engine.