Abstract: Systems, methods, and non-transitory media are provided for dynamically predicting visibility of freights in a constrained environment. An example method can include determining attributes associated with a load transported by a carrier from a source to a destination, the attributes including an identity of the carrier, an identity of an industry associated with the load, an identity of a shipper of the load, load characteristics, and/or a pickup time of the load; based on the attributes, predicting a route the carrier will follow when transporting the load to the destination, at least a portion of the route being predicted without data indicating an actual presence of the carrier within the portion of the route, the data including location measurements from a device associated with the carrier and/or a location update from the carrier; and generating a tracking interface identifying the route the carrier is predicted to follow.

Abstract: Systems, methods, and non-transitory media are provided for dynamically predicting visibility of freights in a constrained environment. An example method can include determining attributes associated with a load transported by a carrier from a source to a destination, the attributes including an identity of the carrier, an identity of an industry associated with the load, an identity of a shipper of the load, load characteristics, and/or a pickup time of the load; based on the attributes, predicting a route the carrier will follow when transporting the load to the destination, at least a portion of the route being predicted without data indicating an actual presence of the carrier within the portion of the route, the data including location measurements from a device associated with the carrier and/or a location update from the carrier; and generating a tracking interface identifying the route the carrier is predicted to follow.

Abstract: Systems, methods, and non-transitory media are provided for dynamically predicting visibility of freights in a constrained environment. An example method can include determining attributes associated with a load transported by a carrier from a source to a destination, the attributes including an identity of the carrier, an identity of an industry associated with the load, an identity of a shipper of the load, load characteristics, and/or a pickup time of the load; based on the attributes, predicting a route the carrier will follow when transporting the load to the destination, at least a portion of the route being predicted without data indicating an actual presence of the carrier within the portion of the route, the data including location measurements from a device associated with the carrier and/or a location update from the carrier; and generating a tracking interface identifying the route the carrier is predicted to follow.

Abstract: Systems, methods, and non-transitory media are provided for dynamically predicting visibility of freights in a constrained environment. An example method can include determining attributes associated with a load transported by a carrier from a source to a destination, the attributes including an identity of the carrier, an identity of an industry associated with the load, an identity of a shipper of the load, load characteristics, and/or a pickup time of the load; based on the attributes, predicting a route the carrier will follow when transporting the load to the destination, at least a portion of the route being predicted without data indicating an actual presence of the carrier within the portion of the route, the data including location measurements from a device associated with the carrier and/or a location update from the carrier; and generating a tracking interface identifying the route the carrier is predicted to follow.

Abstract: Systems, methods, and non-transitory media are provided for dynamically predicting visibility of freights in a constrained environment. An example method can include determining attributes associated with a load transported by a carrier from a source to a destination, the attributes including an identity of the carrier, an identity of an industry associated with the load, an identity of a shipper of the load, load characteristics, and/or a pickup time of the load; based on the attributes, predicting a route the carrier will follow when transporting the load to the destination, at least a portion of the route being predicted without data indicating an actual presence of the carrier within the portion of the route, the data including location measurements from a device associated with the carrier and/or a location update from the carrier; and generating a tracking interface identifying the route the carrier is predicted to follow.

Abstract: Embodiments are directed to identifying active compounds for a targeted medium from a library of compounds. In one scenario, a computer system receives high throughput screening (HTS) data for a subset of compounds that have been HTS-screened. The computer system determines labels for a subset of compounds based on labels identified in the HTS-screened compounds as being part of an active class or part of an inactive class, access chemical features corresponding to the HTS-screened compounds, apply Fuzzy logic membership functions to calculate membership values for active and inactive compounds to determine the degree to which each compound belongs to the active class or to the inactive class, train an artificial neural network (ANN) to identify active compounds in silico based on the Fuzzy logic membership functions, and process another subset of compounds in silico to identify active and inactive compounds using the trained artificial neural network.

Abstract: A method performing voice based actions by an electronic device includes receiving a voice command from a user; determining a relationship between the voice command and a context a historic voice command of the user, and performing an action by executing the voice command based on the context of the historic voice command.

Abstract: A method performing voice based actions by an electronic device includes receiving a voice command from a user; determining a relationship between the voice command and a context a historic voice command of the user, and performing an action by executing the voice command based on the context of the historic voice command.

Abstract: Embodiments are directed to identifying active compounds for a targeted medium from a library of compounds. In one scenario, a computer system receives high throughput screening (HTS) data for a subset of compounds that have been HTS-screened. The computer system determines labels for a subset of compounds based on labels identified in the HTS-screened compounds as being part of an active class or part of an inactive class, access chemical features corresponding to the HTS-screened compounds, apply Fuzzy logic membership functions to calculate membership values for active and inactive compounds to determine the degree to which each compound belongs to the active class or to the inactive class, train an artificial neural network (ANN) to identify active compounds in silico based on the Fuzzy logic membership functions, and process another subset of compounds in silico to identify active and inactive compounds using the trained artificial neural network.