Abstract: One variation of a system for implementing a key-value data store includes one or more processors, storage media and instructions stored in the storage media which, when executed by the system cause the system to: receive a request store a particular key-value item; request a first networked distributed data storage system to store the particular key-value item; based on a determination that a set of one or more offload criteria is satisfied: retrieve a first set of key-value items from the first networked distributed data storage system, and request a second networked distributed data storage system to store the first set of key-value items in a first set of one or more data objects. The first networked distributed data storage system can have a lower data write latency and a higher data storage cost than the second networked distributed data storage system.

Abstract: A deep learning process receives desired process values associated with the one or more process stations. The deep learning processor receives desired target values for one or more key performance indicators of the manufacturing process. The deep learning processor simulates the manufacturing process to generate expected process values and expected target values for the one or more key performance indicators to optimize the one or more key performance indicators. The simulating includes generating a proposed state change of at least one processing parameter of the initial set of processing parameters. The deep learning processor determines that expected process values and the expected target values are within an acceptable limit of the desired process values and the desired target values. Based on the determining, the deep learning processes causes a change to the initial set of processing parameters based on the proposed state change.

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: 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: 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: Aspects of the disclosure relate to parking behaviors and maneuvering a vehicle in an autonomous driving mode accordingly. For instance, a pullover location for the vehicle to stop and wait for a passenger may be identified. The vehicle may be maneuvered in the autonomous driving mode in order to pull over by pulling forward into the pullover location. Whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle may be determined based on context for the pull over with respect to the passenger. The vehicle may be maneuvered in the autonomous driving mode in reverse based on the determination of whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle.

Abstract: A system and method in accordance with example embodiments may include systems and methods for a cloud-based analytics platform. The cloud-based analytics platform may allow the manual and automatic uploading to and/or downloading from a cloud server. The platform may include single sign-on (SSO) capabilities such that a user may have one set of credentials to access data from the cloud-based analytics and/or data stored locally. The platform may include data validation and processing in order to provide real-time feedback on uploads based on file type, file size, access rights, extracted data, and transformed data.

Abstract: An integrated circuit includes first and second bus terminals, a pass-gate transistor, first and rising time accelerator (RTA) control circuits, and first and second falling time accelerator (FTA) control circuits. The pass-gate transistor couples between the first and second bus terminals. The first RTA control circuit couples to the first bus terminal, detects a rising edge on the first bus terminal, and accelerates the rising edge on the first bus terminal. The first FTA control circuit couples to the first bus terminal, detects a falling edge on the first bus terminal having a slope below a threshold, and accelerates the falling edge on the first bus terminal. The second RTA and FTA control circuits function similar to the first RTA and FTA control circuits but with respect to the second bus terminal.

Abstract: Aspects of the disclosure relate generally to effortlessly switching between user accounts. For example, a user may access an application on their computing device. Within the application the user may have multiple user accounts. The application may display a plurality of indicators that signify each user account associated with that application. In this regard, the user may perform a swiping or tapping motion to select a particular user account to switch to. A transitional stage may take place that changes a first background and details associated with a first user account to a second background and details associated with a second user account. When the transition is complete, the user is able to access and perform functions associated with the second user account. The user may switch to another user account using a similar swiping or tapping motion.

Abstract: An inspection apparatus includes a specimen stage configured to retain a specimen, at least three imaging devices arranged in a triangular array positioned above the specimen stage, each of the at least three imaging devices configured to capture an image of the specimen, one or more sets of lights positioned between the specimen stage and the at least three imaging devices, and a control system in communication with the at least three imaging devices.

Abstract: Disclosed is a new approach for client-side contextual engagement with a website visitor. A browser loads a page containing a reference to a script file implementing a visitor client. The visitor client may store visitor client data including site rules and a visitor profile locally on the client device. The visitor client may include a context monitor for calling the decision engine, a decision engine for evaluating the site rules relative to a context defined at least partially by events occurring on the page and the visitor profile, and a widget manager for managing visitor client data persisted on the client device. The content monitor may call the decision engine when the context changes. The decision engine may determine, in real time and relative to the context, that proactive or reactive engagement(s) may be appropriate and call the widget manager to launch and display corresponding engagement channel(s) on the page.

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 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: A simulated process is initiated. The simulated process includes generating, by an emulator, a control signal based on external inputs. The simulated process further includes processing, by a simulator, the control signal to generate simulated response data. The simulated process further includes generating, by a deep learning processor, expected behavioral pattern data based on the simulated response data. An actual process is initiated by initializing setpoints for a process station in a manufacturing system. The actual process includes generating, by the deep learning processor, actual behavioral pattern data based on actual process data from the at least one process station. The deep learning processor compares the expected behavioral pattern to the actual behavioral pattern. Based on the comparing, the deep learning processor determines that anomalous activity is present in the manufacturing system. Based on the anomalous activity being present, the deep learning processor initiates an alert protocol.

Abstract: This disclosure relates to devices and kits for delivery of a fluid. In some embodiments, the fluid is useful in facilitating and maintaining hemostasis.

Abstract: The present disclosure provides an isolated or purified SARS-CoV-2 Receptor Binding Domain (RBD) polypeptide and cells for producing such a polypeptide, as well as compositions and methods thereof.

Abstract: A file access system for user defined functions (UDFs) can be implemented on a distributed database system. The system can store UDF signatures and interfaces (e.g., classes, sub-classes) that can be called by other users. Upon a UDF being called, one or more interface objects (e.g., InputStream) can be created and requests transferred to a execution node via a network channel. The execution node can implement multiple threads that are authorized and download file data from a staging location (e.g., internal stage, external stage) concurrently.

Abstract: Techniques for coordinating movements of various objects in an electronic document are disclosed. A first-type object remains stationary so that it becomes outside a content boundary of the electronic document, as the content boundary is pulled inside an edge of a window displaying the electronic document. A second-type object remains stationary until the content boundary reaches the edge of the window and then moves along with the scrolled electronic document as the content boundary is pulled inside the edge of the window. A third-type object and a fourth-type object may show different movement behaviors.