Abstract: Clock generation circuit generating multiple divided signals satisfying respective desired offsets. A phase locked loop (PLL) is used to generate a PLL output having a frequency which is a desired multiple of that of a reference clock. The circuit divides the PLL output by a corresponding divisor to generate a corresponding divided signal, wherein each divided signal is offset from a common reference by at least an associated desired time offset. The common reference is timed with respect to the reference clock when the reference clock is available and with respect to a time reference signal otherwise. This arrangement is extended to use the internal time reference signal even for the cases where the reference clock is present by blocking the reference clock while the output systems across PLLs are aligned using the internal time reference signal to ensure desired offsets across different PLLs with a small uncertainty.

Abstract: A device may receive content data, a first model, and a second model. The first model may be trained on different types of metadata than the second model. The content data may include a first identifier of a first content item and a first set of metadata associated with the first content item. The device may process the first set of metadata to generate first recommendations from the first model and second recommendations from the second model. The device may provide the first identifier and a combination of the first recommendations and the second recommendations to client devices. The device may receive, from the client devices, user-generated target recommendations based on the combination. The device may process the user-generated target recommendations, the first recommendations, and the second recommendations, to provide feedback to update the first model and the second model.

Abstract: A device may receive content data, a first model, and a second model. The first model may be trained on different types of metadata than the second model. The content data may include a first identifier of a first content item and a first set of metadata associated with the first content item. The device may process the first set of metadata to generate first recommendations from the first model and second recommendations from the second model. The device may provide the first identifier and a combination of the first recommendations and the second recommendations to client devices. The device may receive, from the client devices, user-generated target recommendations based on the combination. The device may process the user-generated target recommendations, the first recommendations, and the second recommendations, to provide feedback to update the first model and the second model.

Abstract: A device may receive content data, a first model, and a second model. The first model may be trained on different types of metadata than the second model. The content data may include a first identifier of a first content item and a first set of metadata associated with the first content item. The device may process the first set of metadata to generate first recommendations from the first model and second recommendations from the second model. The device may provide the first identifier and a combination of the first recommendations and the second recommendations to client devices. The device may receive, from the client devices, user-generated target recommendations based on the combination. The device may process the user-generated target recommendations, the first recommendations, and the second recommendations, to provide feedback to update the first model and the second model.

Abstract: A device may receive content data, a first model, and a second model. The first model may be trained on different types of metadata than the second model. The content data may include a first identifier of a first content item and a first set of metadata associated with the first content item. The device may process the first set of metadata to generate first recommendations from the first model and second recommendations from the second model. The device may provide the first identifier and a combination of the first recommendations and the second recommendations to client devices. The device may receive, from the client devices, user-generated target recommendations based on the combination. The device may process the user-generated target recommendations, the first recommendations, and the second recommendations, to provide feedback to update the first model and the second model.

Abstract: A device may receive content data, a first model, and a second model. The first model may be trained on different types of metadata than the second model. The content data may include a first identifier of a first content item and a first set of metadata associated with the first content item. The device may process the first set of metadata to generate first recommendations from the first model and second recommendations from the second model. The device may provide the first identifier and a combination of the first recommendations and the second recommendations to client devices. The device may receive, from the client devices, user-generated target recommendations based on the combination. The device may process the user-generated target recommendations, the first recommendations, and the second recommendations, to provide feedback to update the first model and the second model.