Abstract: Whether to train a new neural network model can be determined based on similarity estimates between a sample data set and a plurality of source data sets associated with a plurality of prior-trained neural network models. A cluster among the plurality of prior-trained neural network models can be determined. A set of training data based on the cluster can be determined. The new neural network model can be trained based on the set of training data.

Abstract: Methods and systems for easily establishing or switching between wireless connections by sending a Bluetooth Low-Energy (BLE) command from a first BLE enabled device to a second BLE enabled device, where the BLE command causes the second BLE enabled device to attempt to establish a Bluetooth Classic connection with one or more source devices from a list of source devices that have previously been paired with the second BLE device. The first BLE device is configured to obtain or receive the list from the second BLE device, and in response to one or more triggers, send the BLE command. The list of previously paired source devices may be presented to a user via a display screen with touch-screen functionality, and in some examples, may utilize one or more algorithms to intelligently order the available source devices based on a variety of factors.

Abstract: Methods and systems for easily establishing or switching between wireless connections by sending a Bluetooth Low-Energy (BLE) command from a first BLE enabled device to a second BLE enabled device, where the BLE command causes the second BLE enabled device to attempt to establish a Bluetooth Classic connection with one or more source devices from a list of source devices that have previously been paired with the second BLE device. The first BLE device is configured to obtain or receive the list from the second BLE device, and in response to one or more triggers, send the BLE command. The list of previously paired source devices may be presented to a user via a display screen with touch-screen functionality, and in some examples, may utilize one or more algorithms to intelligently order the available source devices based on a variety of factors.

Abstract: Whether to train a new neural network model can be determined based on similarity estimates between a sample data set and a plurality of source data sets associated with a plurality of prior-trained neural network models. A cluster among the plurality of prior-trained neural network models can be determined. A set of training data based on the cluster can be determined. The new neural network model can be trained based on the set of training data.

Abstract: Frangible fasteners with flexible connectors for unmanned aircraft, and associated systems and methods are disclosed. A representative aircraft includes a fuselage portion, a wing portion, a winglet carried by the wing portion, and a frangible fastener coupling the winglet portion to the wing portion. The frangible fastener can include an outer body with a first portion in contact with the wing portion, a second portion in contact with the winglet portion, and a frangible portion between the first and second portions. A flexible member is positioned at least partially within the outer body and is connected to the first portion so as to extend through and out of the second portion. A stop element is carried by the flexible member.

Abstract: Techniques regarding autonomously facilitating the selection of one or more transfer models to enhance the performance of one or more machine learning tasks are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an assessment component that can assess a similarity metric between a source data set and a sample data set from a target machine learning task. The computer executable components can also comprise an identification component that can identify a pre-trained neural network model associated with the source data set based on the similarity metric to perform the target machine learning task.

Abstract: Frangible fasteners with flexible connectors for unmanned aircraft, and associated systems and methods are disclosed. A representative aircraft includes a fuselage portion, a wing portion, a winglet carried by the wing portion, and a frangible fastener coupling the winglet portion to the wing portion. The frangible fastener can include an outer body with a first portion in contact with the wing portion, a second portion in contact with the winglet portion, and a frangible portion between the first and second portions. A flexible member is positioned at least partially within the outer body and is connected to the first portion so as to extend through and out of the second portion. A stop element is carried by the flexible member.

Abstract: Interoperability is enabled between participants in a network by determining values associated with a value metric defined for at least a portion of the network. Information flow is directed between two or more of the participants based at least in part on semantic models corresponding to the participants and on the values associated with the value metric. The semantic models may define interactions between the participants and define at least a portion of information produced or consumed by the participants. The determination of the values and the direction of the information flow may be performed multiple times in order to modify the one or more value metrics. The direction of information flow may allow participants to be deleted from the network, may allow participants to be added to the network, or may allow behavior of the participants to be modified.

Abstract: Interoperability is enabled between participants in a network by determining values associated with a value metric defined for at least a portion of the network. Information flow is directed between two or more of the participants based at least in part on semantic models corresponding to the participants and on the values associated with the value metric. The semantic models may define interactions between the participants and define at least a portion of information produced or consumed by the participants. The determination of the values and the direction of the information flow may be performed multiple times in order to modify the one or more value metrics. The direction of information flow may allow participants to be deleted from the network, may allow participants to be added to the network, or may allow behavior of the participants to be modified.

Abstract: Described is a method and apparatus for obtaining accurate, timely information for event detection and prediction based on autonomous opportunism. The objective is to make the best possible use of all available resources at the time of acquisition, including historical data, multiple sensors, and multiresolution acquisition capabilities, under a given set of processing and communication bandwidth constraints. This method (and the corresponding apparatus) fuses multiple adaptively acquired data sources to prepare information for use by decision support models. The onboard data acquisition schedule is constructed to maximize the prediction accuracy of the decision models, which are designed to operate progressively, utilizing data representations consisting of multiple abstraction levels and multiple resolutions.

Abstract: Described is a method and apparatus for obtaining accurate, timely information for event detection and prediction based on autonomous opportunism. The objective is to make the best possible use of all available resources at the time of acquisition, including historical data, multiple sensors, and multiresolution acquisition capabilities, under a given set of processing and communication bandwidth constraints. This method (and the corresponding apparatus) fuses multiple adaptively acquired data sources to prepare information for use by decision support models. The onboard data acquisition schedule is constructed to maximize the prediction accuracy of the decision models, which are designed to operate progressively, utilizing data representations consisting of multiple abstraction levels and multiple resolutions.