Abstract: Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.

Abstract: Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.

Abstract: Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.

Abstract: A piston for a combustion engine having an oil ring groove on the exterior of the piston which communicates via oil holes with an oil trap extending around the interior of the piston. The oil trap captures lubricant on the downward stroke and pumps lubricant to the piston and cylinder walls on the upward stroke. An oil ring assembly having a wiper, expander and oil ring sections may be provided in the oil ring groove. A bridge may also be provided to support the cylinder head.

Abstract: a flexible, bag-like, receptacle for use when removing a vehicle oil filter. The receptacle has a body which is larger at the open upper end to receive the filter and to catch drips and spills. After the filter is removed by grasping the exterior of the receptacle and contained filter, the contents may be sealed for disposal. In one embodiment, the receptacle has an integrally formed glove or mitt.

Abstract: A piston for a combustion engine having an oil ring groove on the exterior of the piston which communicates via oil holes with an oil trap extending around the interior of the piston. The oil trap captures lubricant on the downward stroke and pumps lubricant to the piston and cylinder walls on the upward stroke. An oil ring assembly having a wiper, expander and oil ring sections may be provided in the oil ring groove. A bridge may also be provided to support the cylinder head.