Abstract: A chatbot designer user interface (UI) has a visual authoring canvas that shows visual elements indicative of a logical flow of processing in a chatbot application. The designer UI receives an authoring input identifying a language processing system that is to be used during runtime. The chatbot application is configured to use the identified language processing system.

Abstract: A visual trigger node, indicative of a trigger, is displayed on a first display portion. A trigger configuration user interface is also displayed. A property input, indicative of a property that activates the trigger, is detected and code is generated to map the trigger to the property.

Abstract: A visual bot designer displays a user interface that has a visual authoring canvas and a property pane. It can also have a serialized file display pane. A user can provide authoring inputs on any of the user interfaces, and the visual bot designer computing system generates and displays updates on the other parts of the user interface.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for routing events of an event stream in a stream processing system. One of the methods includes receiving, by a router, an event stream of events; identifying, for each event, by the router, a respective partition of context data that includes context data related to the event and providing the event to a respective local modeler that stores the partition of context data identified for the event in operational memory of the local modeler; processing, by each local modeler, events received from the router and aggregating information associated with each event to generate aggregated information; providing, by one or more of the local modelers, to a central modeler, the respective aggregated information; and determining, by the central modeler, a plurality of parameters of a machine learning model using the received aggregated information.

Abstract: A visual bot designer displays a user interface that has a visual authoring canvas and a property pane. It can also have a serialized file display pane. A user can provide authoring inputs on any of the user interfaces, and the visual bot designer computing system generates and displays updates on the other parts of the user interface.

Abstract: A chatbot designer user interface (UI) has a visual authoring canvas that shows visual elements indicative of a logical flow of processing in a chatbot application. The designer UI receives an authoring input identifying a language processing system that is to be used during runtime. The chatbot application is configured to use the identified language processing system.

Abstract: A chatbot designer user interface (UI) has a visual authoring canvas that shows visual elements indicative of a logical flow of processing in a chatbot application. The designer UI receives an authoring input identifying a language processing system that is to be used during runtime. The chatbot application is configured to use the identified language processing system.

Abstract: A visual bot designer displays a user interface that has a visual authoring canvas and a property pane. It can also have a serialized file display pane. A user can provide authoring inputs on any of the user interfaces, and the visual bot designer computing system generates and displays updates on the other parts of the user interface.

Abstract: A visual trigger node, indicative of a trigger, is displayed on a first display portion. A trigger configuration user interface is also displayed. A property input, indicative of a property that activates the trigger, is detected and code is generated to map the trigger to the property.

Abstract: A chatbot designer user interface (UI) has a visual authoring canvas that shows visual elements indicative of a logical flow of processing in a chatbot application. The designer UI receives an authoring input identifying a language processing system that is to be used during runtime. The chatbot application is configured to use the identified language processing system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for stream processing. One method includes receiving an event stream of events by a first plurality of local modelers of a stream processing system. Each local modeler processes a portion of received events of the event stream according to a first set of operations, the operations including aggregating information associated with each event to generate aggregated information. One or more local modelers provide, to a first central modeler executing on the system, the respective aggregated information generated by one or more of the local modelers. A set of parameters of a respective machine learning model is determined using the received aggregated information.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for stream processing. One method includes receiving an event stream of first events by a first plurality of first local modelers of a stream processing system. Each local modeler processes a portion of received events of the event stream according to a first set of operations, the operations including aggregating information associated with each event to generate first aggregated information. A second plurality of second local modelers similarly generates second aggregated information from an event stream of second events. First and second local modelers provide, to a first central modeler, first and second aggregated information. A set of parameters of a respective machine learning model is determined by the first central modeler using the received aggregated information.

Abstract: A computer system for processing bidirectional characters includes a processor and memory. An application stored in the memory includes instructions that are executable by the processor and memory and that are configured to determine an embedding direction and at least one of output, export or display data including characters using bidirectional code. While at least one of outputting, exporting or displaying the data, the instructions are configured to parse a multi-segment data value including a plurality of segments and a delimiter arranged between adjacent ones of the plurality of segments. The delimiter includes two or more neutral type characters that are arranged immediately adjacent to one another. The delimiter causes the bidirectional code to apply the embedding direction to the delimiters of the multi-segment data value to maintain relative ordering of adjacent ones of the segments in the multi-segment data value.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for routing events of an event stream. One of the methods includes operations of receiving, by a router, events of an event stream; providing each event, by the router, to a respective local modeler selected by the router according to an initial routing strategy, the respective local modeler being selected from multiple local modelers; aggregating, by each local modeler in parallel, information associated with each event received by the local modeler to generate aggregated information; providing, to a central modeler, the aggregated information generated by the one or more local modelers; determining, by the central modeler, parameters of a machine learning model using the aggregated information received by the central modeler and generating an updated routing strategy based on the parameters of the machine learning model; and providing the updated routing strategy to the router.

Abstract: A computer system for processing bidirectional characters includes a processor and memory. An application stored in the memory includes instructions that are executable by the processor and memory and that are configured to determine an embedding direction and at least one of output, export or display data including characters using bidirectional code. While at least one of outputting, exporting or displaying the data, the instructions are configured to parse a multi-segment data value including a plurality of segments and a delimiter arranged between adjacent ones of the plurality of segments. The delimiter includes two or more neutral type characters that are arranged immediately adjacent to one another. The delimiter causes the bidirectional code to apply the embedding direction to the delimiters of the multi-segment data value to maintain relative ordering of adjacent ones of the segments in the multi-segment data value.

Abstract: Pharmaceutical formulations comprising an inhibitor of poly(ADP)-ribose) polymerase-1 (PARP1) and their use for the treatment of major depressive disorder and conditions that share at least one of the two major defining symptoms of major depressive disorder.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for stream processing with contextual data affinity. One of the methods includes receiving an event at a computing node of a stream processing system that includes one or more computing nodes and data representing a topology of functional modules. At a particular functional module of the topology, an operation that requests access to context data related to the event is determined based on a data attribute of the event. The context data is partitioned among the one or more computing nodes. Context data related to the event is obtained from the computing node. The operation of the particular functional module is processed using the context data, and a modified event is generated. The modified event is provided to a subsequent functional module according to the topology of functional modules.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for routing events of an event stream in a stream processing system. One of the methods includes receiving, by a router, an event stream of events; identifying, for each event, by the router, a respective partition of context data that includes context data related to the event and providing the event to a respective local modeler that stores the partition of context data identified for the event in operational memory of the local modeler; processing, by each local modeler, events received from the router and aggregating information associated with each event to generate aggregated information; providing, by one or more of the local modelers, to a central modeler, the respective aggregated information; and determining, by the central modeler, a plurality of parameters of a machine learning model using the received aggregated information.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for routing events of an event stream. One of the methods includes operations of receiving, by a router, events of an event stream; providing each event, by the router, to a respective local modeler selected by the router according to an initial routing strategy, the respective local modeler being selected from multiple local modelers; aggregating, by each local modeler in parallel, information associated with each event received by the local modeler to generate aggregated information; providing, to a central modeler, the aggregated information generated by the one or more local modelers; determining, by the central modeler, parameters of a machine learning model using the aggregated information received by the central modeler and generating an updated routing strategy based on the parameters of the machine learning model; and providing the updated routing strategy to the router.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting trends in event streams. One method includes generating a first set of parameters of a machine learning model from a first system processing an event stream, the first system comprising a first central modeler that receives aggregated information from a first plurality of local modelers; generating a second set of parameters of the machine learning model from a second system processing the event stream, the second system comprising a second central modeler that receives aggregated information from a second plurality of local modelers; determining a difference between the first set of parameters and the second set of parameters; and determining that the difference is greater than a threshold amount and as a consequence outputting information identifying a trend in the event stream.