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 soap dispenser system includes a fixture disposed above a countertop and a soap container disposed below the countertop. The fixture includes a main body and a mount attached to the countertop. The main body includes a soap dispensing element that is fluidically coupled to the soap container via a fluid supply line. The mount includes an indication system and a refill port for accessing the soap container. The main body is removably coupled to the mount in order to expose the refill port and access the soap container. The indication system is configured to indicate a power status of the system and a soap level status of the soap container.

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: 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.

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: 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 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.