Abstract: Certain aspects of the present disclosure provide techniques and apparatus for a training and using machine learning models in multi-device network environments. An example computer-implemented method for network communications performed by a host device includes extracting a feature set from a data set associated with a client device using a client-device-specific feature extractor, wherein the feature set comprises a subset of features in a common feature space, training a task-specific model based on the extracted feature set and one or more other feature sets associated with other client devices, wherein the feature sets associated with the other client devices comprise one or more subsets of features in the common feature space, and deploying, to each respective client device of a plurality of client devices, a respective version of the task-specific model.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. Voice data from a first user is received. In response to determining that the voice data includes an utterance of a defined keyword, a user verification score is generated by processing the voice data using a first user verification machine learning (ML) model, and a quality of the voice data is determined. In response to determining that the user verification score and determined quality satisfy one or more defined criteria, a second user verification ML model is updated based on the voice data.

Abstract: A health monitoring, assessing and response system includes an interface and a controller. The interface is configured to receive a signal from a sensor disposed in a substrate processing system. The controller includes a health index module. The health index module is configured to perform an algorithm including: obtaining a window and a boundary threshold; monitoring the signal output from the sensor; determining whether the signal has crossed the boundary threshold; updating a health index component, where the health index component is a binary value and transitioned between HIGH and LOW values in response to the signal crossing the boundary threshold; and generating a health index value based on the health index component and decreasing the health index value from 100% to 0% over a duration of at least the window. The controller is configured to perform a countermeasure based on the health index value.

Abstract: Textured silicon components of a semiconductor processing chamber having hillock-shaped or pyramid-shaped structures on its surface, and a method of texturing such silicon components. The silicon component can be selectively textured using chemical means to form the hillock-shaped structures to increase the surface area of the silicon component to improve polymer adhesion.

Abstract: A system and method provides a computer-implemented messaging service. Users subscribe to message channels. A message queue buffers messages to a router. The router determines whether characteristics of a first message match criteria specified in subscription requests, and may route the first message to one or more sending agents based on modes of communication specified in the subscription service requests. The sending agent(s) generate a second message formatted for the specified modes of communication based on the first message. Message load balancing, message queuing, message routing, and message sending services may all be independently scaled to adjust for demand.

Abstract: A system and method provides a computer-implemented messaging service. Users subscribe to message channels. A message queue buffers messages to a router. The router determines whether characteristics of a first message match criteria specified in subscription requests, and may route the first message to one or more sending agents based on modes of communication specified in the subscription service requests. The sending agent(s) generate a second message formatted for the specified modes of communication based on the first message. Message load balancing, message queuing, message routing, and message sending services may all be independently scaled to adjust for demand.

Abstract: A force control module, assembly, or device for controlling a force required to actuate an electromechanical actuator or a set of electromechanical actuators. The electromechanical actuator is for example a computer mouse button, a keypad, or a joystick button. The force control module includes a lever element that is couplable to the electromechanical actuator, and a fulcrum element that is engageable with the lever element at a pivot point or fulcrum point. A set of displacement control units is configured and disposed for controlling the displacement of the fulcrum element relative to the lever element. A displacement of the fulcrum element relative to the lever element varies a position of the fulcrum point. The force required for actuating the electromechanical actuator is at least partially dependent upon the position of the fulcrum point.

Abstract: A force control module, assembly, or device for controlling a force required to actuate an electromechanical actuator or a set of electromechanical actuators. The electromechanical actuator is for example a computer mouse button, a keypad, or a joystick button. The force control module includes a lever element that is couplable to the electromechanical actuator, and a fulcrum element that is engageable with the lever element at a pivot point or fulcrum point. A displacement of the fulcrum element relative to the lever element varies a position of the fulcrum point. The force required for actuating the electromechanical actuator is at least partially dependent upon the position of the fulcrum point. By displacing the fulcrum element relative to the lever element, and hence varying the position of the fulcrum point, a user can vary the force required for actuating the electromechanical actuator.