Abstract: Self-delivering digester 100s with self-delivery of accumulated solids are described. A primary waste vessel includes a feeding port for kitchen waste, and an upper output component that connects to a top of the primary waste vessel. The upper output component includes a gas output path from a top of the upper output component, and a floating solids output path that delivers floating solids that overflow from the top of the primary waste vessel to a secondary vessel thereby preventing clogging of the gas output path.

Abstract: A dock coupling assembly that connects multiple dock segments to one another, wherein the dock coupling assembly comprising a female attachment bracket having a base portion and an offset portion that is connected to the base portion and is out of alignment with the base portion. The female attachment bracket further comprises a vertical flange on an inner surface that travels from a top edge to a bottom edge, and wherein the dock coupling assembly also comprises a male attachment bracket comprising a first outwardly extending engagement member and a second outwardly extending engagement member located on the base plate outer surface along the proximal edge, and wherein the male attachment bracket further comprises a base plate vertical flange that travels from a base plate top edge to a base plate bottom edge.

Abstract: A machine learning feature studio comprises a user interface configured to allow a user to define features associated with an entity. The features are calculated using historical or real-time data stored in an event store and associated with the entity. Visualizations and values of the calculated feature are displayed in the user interface and the user may interact with the features, such as to edit and compare them. The user commits the features to the project associated with a machine learning model and selects to export the project. Feature vectors may are calculated using the committed features and are exported to a production environment.

Abstract: The present invention provides recombinant viral segments comprising an artificial intron, DNA constructs encoding these viral segments, and recombinant viruses comprising these viral segments. Also provided are methods of making and using the recombinant viruses described herein.

Abstract: A system for federated learning comprises a first computing node comprising a first database configured to store data indicative of events associated with a particular subset of a plurality of entities. The first computing node may be configured at least to receive a second set of machine learning features from a second computing node comprising machine learning features generated by data indicative of events associated with a different particular subset of a plurality of entities stored by the second computing node. The first computing node may be configured to generate a first set of machine learning features using the data indicative of events stored in the first database combined with the second set of machine learning features. The first computing node may be configured to cause a machine learning model associated with the first computing node to be trained with the first set of machine learning features.

Abstract: Self-delivering digester 100s with self-delivery of accumulated solids are described. A primary waste vessel includes a feeding port for kitchen waste, and an upper output component that connects to a top of the primary waste vessel. The upper output component includes a gas output path from a top of the upper output component, and a floating solids output path that delivers floating solids that overflow from the top of the primary waste vessel to a secondary vessel thereby preventing clogging of the gas output path.

Abstract: A machine learning feature studio comprises a user interface configured to allow a user to define features associated with an entity. The features are calculated using historical or real-time data stored in an event store and associated with the entity. Visualizations and values of the calculated feature are displayed in the user interface and the user may interact with the features, such as to edit and compare them. The user commits the features to the project associated with a machine learning model and selects to export the project. Feature vectors may are calculated using the committed features and are exported to a production environment.

Abstract: Machine learning feature engineering systems and methods comprise an event ingestion module that receives event data associated with entities. The ingestion module determines which entities are associated with events of the event data. The ingestion module stores the events, grouped by associated entity, in a related event store. A user defines features associated with the entities via an API and/or a feature studio. A feature computation layer determines values for the features based on the grouped events stored to the related event store. The feature computation layer stores the computed feature values and timestamps to a feature store. When new data is received, the feature computation layer computes one or more of the feature values for different times based on the timestamps. Feature vectors are generated using the computed feature values and output to the user via the API and/or feature studio.

Abstract: A system for generating machine learning feature vectors or examples is disclosed herein. The system comprises at least one database configured to store data indicative of events associated with a plurality of entities, an application programming interface (API) server configured to receive a user query from at least one user device, and at least one computing node in communication with the API server and the at least one database. The at least one computing node is configured at least to receive, from the API server and at a first time, a first indication of the user query. The at least one computing node is configured to generate, based at least on the data indicative of events and the first indication of the user query, results associated with the user query, wherein the results comprise one or more feature vectors or examples for use with a machine learning algorithm. The at least one computing node is configured to cause storage of data indicative of the results in the at least one database.

Abstract: A machine learning feature studio comprises a user interface configured to allow a user to define features associated with an entity. The features are calculated using historical or real-time data stored in an event store and associated with the entity. Visualizations and values of the calculated feature are displayed in the user interface and the user may interact with the features, such as to edit and compare them. The user commits the features to the project associated with a machine learning model and selects to export the project. Feature vectors may are calculated using the committed features and are exported to a production environment.

Abstract: A method for generating machine learning training examples using data indicative of events associated with a plurality of entities. The method comprises receiving an indication of one or more selected entities of the plurality of entities, receiving information indicative of selecting one or more prediction times associated with each of the one or more selected entities, and receiving information indicative of selecting one or more label times associated with each of the one or more selected entities. Each of the one or more label times corresponds to at least one of the one or more prediction times, and the one or more label times occur after the corresponding one or more prediction times. Data associated with the one or more prediction times and the one or more label times is extracted from the data indicative of events associated with the plurality of entities.

Abstract: A machine learning feature studio comprises a user interface configured to allow a user to define features associated with an entity. The features are calculated using historical or real-time data stored in an event store and associated with the entity. Visualizations and values of the calculated feature are displayed in the user interface and the user may interact with the features, such as to edit and compare them. The user commits the features to the project associated with a machine learning model and selects to export the project. Feature vectors may are calculated using the committed features and are exported to a production environment.

Abstract: A method for generating machine learning training examples using data indicative of events associated with a plurality of entities. The method comprises receiving an indication of one or more selected entities of the plurality of entities, receiving information indicative of selecting one or more prediction times associated with each of the one or more selected entities, and receiving information indicative of selecting one or more label times associated with each of the one or more selected entities. Each of the one or more label times corresponds to at least one of the one or more prediction times, and the one or more label times occur after the corresponding one or more prediction times. Data associated with the one or more prediction times and the one or more label times is extracted from the data indicative of events associated with the plurality of entities.

Abstract: Machine learning feature engineering systems and methods comprise an event ingestion module that receives event data associated with entities. The ingestion module determines which entities are associated with events of the event data. The ingestion module stores the events, grouped by associated entity, in a related event store. A user defines features associated with the entities via an API and/or a feature studio. A feature computation layer determines values for the features based on the grouped events stored to the related event store. The feature computation layer stores the computed feature values and timestamps to a feature store. When new data is received, the feature computation layer computes one or more of the feature values for different times based on the timestamps. Feature vectors are generated using the computed feature values and output to the user via the API and/or feature studio.