Abstract: A mulling system for a plurality of fenestration units includes a first joining plate configured to attach to a first outer edge of a first fenestration unit. The first joining plate includes a first outer face. The mulling system also includes a second joining plate configured to attach to a second outer edge of a second fenestration unit. The second joining plate includes a second outer face. The second joining plate is configured to engage the first joining plate to join together the first fenestration unit and the second fenestration unit with the first outer face facing the second outer face. The mulling system additionally includes a deflectable seal member attached to the first joining plate and having a sealing surface that faces away from the first outer face. The sealing surface is configured to deflect and seal against the second outer face of the second joining plate.

Abstract: An apparatus and method for an inspection apparatus for inspecting a component. The inspection apparatus including a robotic arm. A micro-XRF instrument having an instrument head coupled to the robotic arm. A seat supporting the component within a scanning area during inspection; and a computer in communication with the robotic arm and the micro-XRF instrument.

Abstract: Systems and methods of improving AI governance are disclosed. One or more sub-contexts associated with a plurality of users are generated from one or more data sources. The one or more sub-contexts represent one or more changes in data that are relevant to assessing one or more risks associated with the plurality of users. One or more sub-contexts are provided as training data to a plurality of models. Each of the models is associated with a confidence score. A probabilistic assessment of the one or more risks associated with the plurality of users is generated based on an application of the plurality of models to additional data pertaining to the plurality of users received in real time. The probabilistic assessment is presented in a dashboard user interface, the dashboard user interface having user interface elements configured to provide insight into how the probabilistic assessment was generated.

Abstract: A system is disclosed herein. The system includes a splitter board. The splitter board includes a microprocessor, a converter, and a bypass relay. The converter includes analog-to-digital circuitry and digital-to-analog circuitry. The bypass relay is configurable between a first state and a second state. In the first state, the bypass relay is configured to direct an input signal to the converter. The converter converts the input signal to a converted input signal and splits the converted input signal into a first portion and a second portion. The first portion is directed to the microprocessor. The second portion is directed to an output port of the splitter board for downstream processes. In the second state, the bypass relay is configured to cause the input signal to bypass the converter. The bypass relay directs the input signal to the output port of the splitter board for the downstream processes.

Abstract: Methods and systems for routing data payloads through a plurality of microservices are disclosed. The method may comprise: receiving a data payload and first data; predicting, by a trained machine learning model based on the first data, a path through a plurality of microservices associated with the data payload; generating a modified data payload by modifying, via the orchestrator service, the data payload to include: a first header, wherein the first header comprises a first microservice destination address associated with a first microservice of the plurality of microservices and a second header nested within the first header, wherein the second header comprises a second microservice destination address associated with a second microservice of the plurality of microservices; forwarding the modified data payload to the first microservice based on the first header for processing; and forwarding the modified data payload to the second microservice based on the second header for processing.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle to enter a driveway. As an example, an instruction to pickup or drop off a passenger at a location may be received. It may be determined that the vehicle is arriving in a lane on an opposite side of a street as the location, the lane having a traffic direction opposite to a traffic direction of a lane adjacent to the location. A difficulty score to maneuver the vehicle to the lane adjacent to the location may be determined, and the difficulty score may be compared to a predetermined difficulty score. Based on the comparison, it may be determined to an available driveway on the same side of the street as the location. The vehicle may be controlled to enter the available driveway.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes a monitoring platform and an analytics platform. The monitoring platform is configured to capture data of an operator during assembly of an article of manufacture. The monitoring platform includes one or more cameras and one or more microphones. The analytics platform is in communication with the monitoring platform. The analytics platform is configured to analyze the data captured by the monitoring platform.

Abstract: Implementations set forth herein relate to providing notifications regarding events that may interrupt content being rendered at an interface. The notifications can be preemptive and/or can indicate a predicted time and/or source for the events. The event can be, for example, a person attempting to contact a user who is viewing content at a display interface. The person can be associated with a food delivery that has been ordered by the user via a delivery application. An application, such an automated assistant application, can predict when the person is expected to arrive with the food delivery, and generate a notification ahead of the person arriving. In some implementations, the notification can be rendered at a scrubber user interface (UI) at a location corresponding to the time that the food delivery is expected to arrive, thereby putting the user on notice of when the streaming content may be interrupted.

Abstract: A node associated with an organization may receive a storage identifier for new credit data associated with an individual. A distributed ledger and distributed data sources may be used to share the new credit data with a network of nodes. The node may update a smart contract with the storage identifier for the new credit data. The node may receive, from a particular device associated with the organization, a request for the new credit data. The node may obtain the storage identifier for the new credit data from the smart contract. The node may obtain the new credit data by using the storage identifier to search the distributed data sources. The node may provide the new credit data to the particular device. The node may perform actions to obtain additional new credit data from the distributed data sources or provide the additional new credit data to the distributed data sources.

Abstract: A microscopy system and method of focusing the same are disclosed herein. The microscopy system may include an objective, and imaging device, an illumination source, an epi-illumination module, and a controller. The imaging device is configured to capture a single image of a specimen positioned on a stage of the microscopy system. The illumination source is configured to illuminate the specimen positioned on the stage. The epi-illumination module includes a focusing mechanism in a first primary optical path of a light generated by the illumination source. The focusing mechanism is tilted in relation to a plane perpendicular to the first primary optical path. The controller is in communication with the illumination source. The controller is configured to focus the microscopy system based on a pattern produced by the focusing mechanism on the single image captured by the imaging device.

Abstract: Implementations set forth herein relate to an automated assistant that can proactively identify and complete tasks that may be associated with an activity with which a user has scheduled the automated assistant to assist with. The tasks can be identified and completed prior to a time that the user has scheduled the automated assistant to assist the user, thereby eliminating certain manual tasks the user may otherwise perform at the scheduled time. When the activity involves communicating with a separate entity, such as another person and/or organization, the automated assistant can initialize communication with the entity prior to the scheduled time that the user requested assistance. A customized GUI can be rendered at an assistant-enabled device to provide the user with an ongoing status of completing various tasks associated with scheduled activity.

Abstract: A training set that includes at least two data types corresponding to operations and control of a manufacturing process is obtained. A deep learning processor is trained to predict expected characteristics of output control signals that correspond with one or more corresponding input operating instructions. A first input operating instruction is received from a first signal splitter. A first output control signal is received from a second signal splitter. The deep learning processor correlates the first input operating instruction and the first output control signal. Based on the correlating, the deep learning processor determines that the first output control signal is not within a range of expected values based on the first input operating instruction. Responsive to the determining, an indication of an anomalous activity is provided as a result of detection of the anomalous activity in the manufacturing process.

Abstract: The technology involves pullover maneuvers for vehicles operating in an autonomous driving mode. A vehicle operating in an autonomous driving mode is able to identify parking locations that may be occluded or otherwise not readily visible. A best case estimation for any potential parking locations, including partially or fully occluded areas, is compared against predictions for identified visible parking locations. This can include calculating a cost for each potential parking location and comparing that cost to a baseline cost. Upon determining that an occluded location would provide a more viable option than any visible parking locations, the vehicle is able to initiate a driving adjustment (e.g., slow down) prior to arriving at the occluded location. This enables the vehicle to minimize passenger discomfort while also providing notice to other road users of an intent to pull over.

Abstract: In an aspect, first interaction data characterizing a first interaction of a user with a mobile device configured to operate an application client can be received. A plurality of segments of users of the application client can be determined, and each of the plurality of segments can be associated with one or more settings that characterize an operating parameter of the application client. The user can be assigned to a first segment of the determined plurality of segments based on a correspondence between a characteristic of the user and a characteristic associated with the first segment. The application client can be modified based on the one or more settings associated with the first segment. The modified application client can be provided for interaction by the user via the mobile device. Related systems, apparatus, techniques, and articles are also described.

Abstract: Aspects of the disclosure provide for enabling autonomous vehicles to pull over into driveways when picking up or dropping off passengers or goods. For instance, a request for a trip identifying a first location and a second location may be received. The first location may be a location of a client computing device, and the second location may be a starting location or a destination for the trip. A user preference for the trip indicating that a pickup for the trip be in a driveway may be identified. That the first location corresponds with the second location may be identified. Based on the determination that the first location corresponds with the second location, dispatch instructions may be to an autonomous vehicle. The dispatch instructions may identify a polygon for a driveway at the second location in order to cause the autonomous vehicle to pull over into the driveway.

Abstract: The computing device obtains a training data set related to a plurality of historic user inputs associated with preferences of one or more services or items from an entity. For each of the one or more services or items, the computing device executes operations to train a plurality of models using the training data set to generate a plurality of recommended models, apply a validation data set to generate a plurality of predictions from the plurality of recommended models, obtain a weight of each metric of a plurality of metrics from the entity, obtain user inputs associated with user preferences, and determine a relevancy score for each metric. The computing device selects a recommended model based on the relevancy score of the selected metric or a combination of selected metrics, generates one or more recommendations for the users, and outputs the one or more generated recommendations to the users.

Abstract: A system and method for generating a training data set for training a machine learning model to detect defects in specimens is described herein. A computing system cause presentation of an image on a device of a user. The image includes at least one defect on an example specimen. The computing system receives an annotated image from the user. The user annotated the image using an input via the device. The input includes a first indication of a location of the defect and a second indication of a class corresponding to the defect. The computing system adjusts the annotated image to standardize the input based on an error profile of the user and the class corresponding to the defect. The computing system uploads the annotated image for training the machine learning model.

Abstract: A system is disclosed herein. The system includes a splitter board. The splitter board includes a microprocessor, a converter, and a bypass relay. The converter includes analog-to-digital circuitry and digital-to-analog circuitry. The bypass relay is configurable between a first state and a second state. In the first state, the bypass relay is configured to direct an input signal to the converter. The converter converts the input signal to a converted input signal and splits the converted input signal into a first portion and a second portion. The first portion is directed to the microprocessor. The second portion is directed to an output port of the splitter board for downstream processes. In the second state, the bypass relay is configured to cause the input signal to bypass the converter. The bypass relay directs the input signal to the output port of the splitter board for the downstream processes.

Abstract: A system is disclosed herein. The system includes a splitter board. The splitter board includes a microprocessor, a converter, and a bypass relay. The converter includes analog-to-digital circuitry and digital-to-analog circuitry. The bypass relay is configurable between a first state and a second state. In the first state, the bypass relay is configured to direct an input signal to the converter. The converter converts the input signal to a converted input signal and splits the converted input signal into a first portion and a second portion. The first portion is directed to the microprocessor. The second portion is directed to an output port of the splitter board for downstream processes. In the second state, the bypass relay is configured to cause the input signal to bypass the converter. The bypass relay directs the input signal to the output port of the splitter board for the downstream processes.