Abstract: Various technologies described herein pertain to detection of an opportune time period to deliver a notification. Responsive to receipt of the notification (e.g., at a user device), analysis of an attention state of a user can be initialized. Further, the opportune time period to deliver the notification can be detected based on the analysis of the attention state of the user. The opportune time period can be during a breakpoint or an influential context. The breakpoint is when the user has switched between tasks and lacks engagement with the tasks. The influential context is a particular context in which the user is available to attend to the notification. Moreover, the notification can be delivered during the opportune time period.

Abstract: Systems, methods, and computer media for machine learning through a symbolic, parallelized stochastic gradient descent (SGD) analysis are provided. An initial data portion analyzer can be configured to perform, using a first processor, SGD analysis on an initial portion of a training dataset. Values for output model weights for the initial portion are initialized to concrete values. Local model builders can be configured to perform, using an additional processor for each local model builder, symbolic SGD analysis on an additional portion of the training dataset. The symbolic SGD analysis uses a symbolic representation as an initial state for output model weights for the corresponding portions of the training dataset. The symbolic representation allows the SGD analysis and symbolic SGD analysis to be performed in parallel. A global model builder can be configured to combine outputs of the local model builders and the initial data portion analyzer into a global model.

Abstract: A “Poll Optimizer” provides automated techniques for performing various combinations of both static and runtime optimizations for crowd-sourced queries including, but not limited to, crowd-sourced opinion-based polls. These optimizations have been observed to improve poll performance by reducing factors such as completion times, monetary costs, and error rates of polls. In various implementations, the Poll Optimizer receives an input query representing a crowd-sourced poll that is formatted as a multi-layer structure (e.g., LINQ-based queries natively supported by .NET languages, JQL-based queries supported by JAVA, etc.). The Poll optimizer then iteratively reduces the multi-layer structure of the input query to construct a reformulated query. This reformulated query is then matched to an optimized execution process selected from a plurality of predefined execution processes.

Abstract: Systems, methods, and computer media for machine learning through a symbolic, parallelized stochastic gradient descent (SGD) analysis are provided. An initial data portion analyzer can be configured to perform, using a first processor, SGD analysis on an initial portion of a training dataset. Values for output model weights for the initial portion are initialized to concrete values. Local model builders can be configured to perform, using an additional processor for each local model builder, symbolic SGD analysis on an additional portion of the training dataset. The symbolic SGD analysis uses a symbolic representation as an initial state for output model weights for the corresponding portions of the training dataset. The symbolic representation allows the SGD analysis and symbolic SGD analysis to be performed in parallel. A global model builder can be configured to combine outputs of the local model builders and the initial data portion analyzer into a global model.

Abstract: Various techniques for evaluating probabilistic assertions are described herein. In one example, a method includes transforming a program, a probabilistic assertion, and an input into an intermediate representation, the intermediate representation including a Bayesian network of nodes representing distributions. The method further includes verifying a probabilistic assertion in the program using the intermediate representation.

Abstract: A “Poll Optimizer” provides automated techniques for performing various combinations of both static and runtime optimizations for crowd-sourced queries including, but not limited to, crowd-sourced opinion-based polls. These optimizations have been observed to improve poll performance by reducing factors such as completion times, monetary costs, and error rates of polls. In various implementations, the Poll Optimizer receives an input query representing a crowd-sourced poll that is formatted as a multi-layer structure (e.g., LINQ-based queries natively supported by .NET languages, JQL-based queries supported by JAVA, etc.). The Poll optimizer then iteratively reduces the multi-layer structure of the input query to construct a reformulated query. This reformulated query is then matched to an optimized execution process selected from a plurality of predefined execution processes.

Abstract: Various technologies described herein pertain to detection of an opportune time period to deliver a notification. Responsive to receipt of the notification (e.g., at a user device), analysis of an attention state of a user can be initialized. Further, the opportune time period to deliver the notification can be detected based on the analysis of the attention state of the user. The opportune time period can be during a breakpoint or an influential context. The breakpoint is when the user has switched between tasks and lacks engagement with the tasks. The influential context is a particular context in which the user is available to attend to the notification. Moreover, the notification can be delivered during the opportune time period.

Abstract: Various technologies described herein pertain to parallel local sequence alignment that aligns a query sequence with a database sequence. The database sequence is segmented into a plurality of stripes. A first processing unit can compute Smith-Waterman values for a first stripe of the database sequence across the query sequence based on a cost function that models biological similarity between sequences. Moreover, a second processing unit can compute Smith-Waterman values for a second stripe of the database sequence across the query sequence based on the cost function. Further, a subset of the Smith-Waterman values for the second stripe of the database sequence across the query sequence can be re-computed based on the cost function (e.g., by the first processing unit or the second processing unit). The subset of the Smith-Waterman values to be re-computed can be determined based on a query sequence length and the cost function.

Abstract: Various technologies described herein pertain to detection of an opportune time period to deliver a notification. Responsive to receipt of the notification (e.g., at a user device), analysis of an attention state of a user can be initialized. Further, the opportune time period to deliver the notification can be detected based on the analysis of the attention state of the user. The opportune time period can be during a breakpoint or an influential context. The breakpoint is when the user has switched between tasks and lacks engagement with the tasks. The influential context is a particular context in which the user is available to attend to the notification. Moreover, the notification can be delivered during the opportune time period.

Abstract: Various techniques for evaluating probabilistic assertions are described herein. In one example, a method includes transforming a program, a probabilistic assertion, and an input into an intermediate representation, the intermediate representation including a Bayesian network of nodes representing distributions. The method further includes verifying a probabilistic assertion in the program using the intermediate representation.

Abstract: Various technologies described herein pertain to detection of an opportune time period to deliver a notification. Responsive to receipt of the notification (e.g., at a user device), analysis of an attention state of a user can be initialized. Further, the opportune time period to deliver the notification can be detected based on the analysis of the attention state of the user. The opportune time period can be during a breakpoint or an influential context. The breakpoint is when the user has switched between tasks and lacks engagement with the tasks. The influential context is a particular context in which the user is available to attend to the notification. Moreover, the notification can be delivered during the opportune time period.

Abstract: Various technologies described herein pertain to parallel local sequence alignment that aligns a query sequence with a database sequence. The database sequence is segmented into a plurality of stripes. A first processing unit can compute Smith-Waterman values for a first stripe of the database sequence across the query sequence based on a cost function that models biological similarity between sequences. Moreover, a second processing unit can compute Smith-Waterman values for a second stripe of the database sequence across the query sequence based on the cost function. Further, a subset of the Smith-Waterman values for the second stripe of the database sequence across the query sequence can be re-computed based on the cost function (e.g., by the first processing unit or the second processing unit). The subset of the Smith-Waterman values to be re-computed can be determined based on a query sequence length and the cost function.