Abstract: Methods, computer-readable media, systems and apparatuses for determining and implementing risk unit usage-based insurance policies. In some arrangements, sensor data associated with vehicle operation data, driving data, and the like, may be received and analyzed to determine a consumption rate of risk units in the risk unit usage-based insurance policy. In some examples, one or more driving behaviors may also be identified (e.g., from the sensor data, historical data, and the like). In some examples, one or more user interfaces may be generated displaying the determined consumption rate and/or driving behaviors. In some arrangements, additional information, such as one or more recommendations for improving (e.g., reducing) consumption rate may be generated and provided via the generated user interface.

Abstract: Methods, computer-readable media, systems and apparatuses for determining and implementing risk unit usage-based insurance policies. In some arrangements, sensor data associated with vehicle operation data, driving data, and the like, may be received and analyzed to determine a consumption rate of risk units in the risk unit usage-based insurance policy. In some examples, one or more driving behaviors may also be identified (e.g., from the sensor data, historical data, and the like). In some examples, one or more user interfaces may be generated displaying the determined consumption rate and/or driving behaviors. In some arrangements, additional information, such as one or more recommendations for improving (e.g., reducing) consumption rate may be generated and provided via the generated user interface.

Abstract: Systems and methods can leverage task-specific unlabeled data to improve downstream performance in data-constrained scenarios. Given a target task, a first technique proposed herein, which can be referred to as task augmentation, uses unlabeled text from the target domain to synthesize a large amount of in-domain training data for an auxiliary task A second technique provides a self-training algorithm, where a model learns to improve itself using its predictions on unlabeled examples.

Abstract: A toy scissors apparatus for modeling compound. The scissors apparatus has two handles, each handle connected to a respective mounting cap and to a respective rotatable scissor blade. Each of the blades is rotatable about its longitudinal axis between two positions about 180° apart so as to enable a choice of two different cutting edge regions for use. To alter cutting edges, the blade is pulled away from the mounting cap against a biasing force created by a spring, and then rotated.