Abstract: The present disclosure provides an engine oil cooling structure of an engine. In the engine oil cooling structure of an engine, the engine includes a crankcase, the crankcase is provided with an oil filter mounting chamber having a cylindrical shape, the cooling structure includes a water channel, a water inlet of the water channel communicates with a water pump, the crankcase is further provided with a main oil channel, the crankcase is further provided with a heat conducting thin wall, an inner cavity of the heat conducting thin wall form the oil filter mounting chamber, the oil filter mounting chamber communicates with the main oil channel, the water channel is arranged around an outer side wall of the heat conducting thin wall, and a water outlet of the water channel communicates with a cooling water channel arranged in the same direction as and adjacent to the main oil channel.

Abstract: Techniques for adaptive multi-level recommendation include processing input features to generate pre-processed input features, processing the input features and the pre-processed input features using a plurality of expert models to generate expert outputs, mixing the input features and the expert outputs to generate mixed expert outputs, processing the mixed expert outputs using a first model of the hierarchical model to generate intermediate outputs, and processing the mixed expert outputs and the intermediate outputs using a second model of the hierarchical model to generate a final output.

Abstract: Techniques for training a hierarchical model include concurrently training a first model and a second model of the hierarchical model using first training data to update first parameters of the first model and second parameters of the second model, wherein output from the first model is provided to the second model. Upon determining that a performance metric has met one or more criteria, the first parameters are frozen to generate frozen first parameters. The second model is then further trained using second training data, wherein the second training data is presented to the first model with the frozen first parameters and the second model.

Abstract: Techniques for inferencing using a hierarchical model include receiving a plurality of inputs for a first model and a second model of the hierarchical model, where the output from the first model is presented to the second model. The method involves presenting the first input to the first model to generate a first intermediate output, and presenting the second input and the first intermediate output to the second model to generate a first output for the hierarchical model. The first intermediate output is cached. Upon receiving a second plurality of inputs, the method checks if the third input matches the first input. If matched, the first intermediate output is retrieved from the cache and presented along with the fourth input to a replica of the second model to generate a second output of the hierarchical model.

Abstract: Various embodiments set forth systems and techniques for training a personalized prediction model. The techniques include generating, based on interaction data associated with one or more users and a first weight associated with the interaction data, a first set of training data; generating, based on the personalized prediction model, a predicted enjoyment signal associated with playback of a digital content item; generating, based on the first set of training data and the predicted enjoyment signal, a second set of training data; and updating one or more parameters of a personalized ranking model based on the second set of training data.