Abstract: Privacy preservation model training includes a plurality of iterative update rounds performed on a model held by a data party of a plurality of data parties participating in training to obtain model data, which includes first shared data and local data corresponding to a shared portion and a dedicated portion of the model, respectively. The iterative training adds a perturbation to the first shared data to perform privacy preservation on at least the first shared data. The first shared data is transmitted to a server, which determines, based on first shared data of the plurality of data parties, second shared data. The shared portion of the model is updated based on the second shared data returned by the server. A next iterative update round is performed based on an updated model or using the updated model as a final model.

Abstract: The disclosure relates to a crystalline solid form of Compound 1. The disclosure also provides methods of preparing said crystalline solid form of Compound 1.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may be a UE or a component thereof that is configured to detect a connection establishment failure associated with a network node. The apparatus may be further configured to apply a set of preconfigured values respectively associated with a set of connection establishment failure control parameters when the connection establishment failure is detected.

Abstract: The present invention relates to crystalline polymorphs of an echinocandin antifungal agent and novel methods for their preparation.

Abstract: Privacy preservation model training includes a plurality of iterative update rounds performed on a model held by a data party of a plurality of data parties participating in training to obtain model data, which includes first shared data and local data corresponding to a shared portion and a dedicated portion of the model, respectively. The iterative training adds a perturbation to the first shared data to perform privacy preservation on at least the first shared data. The first shared data is transmitted to a server, which determines, based on first shared data of the plurality of data parties, second shared data. The shared portion of the model is updated based on the second shared data returned by the server. A next iterative update round is performed based on an updated model or using the updated model as a final model.

Abstract: Implementations of this specification disclose methods and systems for training a privacy protection model. In an implementation, a method comprising: performing one or more times of iterative training on the model based on a training sample held by the data party to obtain model data, transmitting the first shared data to a server for the server to determine second shared data based on the first shared data, receiving the second shared data from the server, updating the shared portion of the model based on the second shared data to obtain an updated shared portion, and generating, based on the updated shared portion, an updated model for performing a next one of the plurality of iterative updates in response to determining that the next one of the plurality of iterative updates is not a last one of the plurality of iterative updates.

Abstract: The present invention relates to crystalline polymorphs of an echinocandin antifungal agent and novel methods for their preparation.

Abstract: Implementations of the present specification provide a method and an apparatus for obtaining a training sample of a first model based on a second model. The method includes obtaining at least one first sample, each first sample including feature data and a label value, the label value corresponding to a predicted value of the first model; and separately inputting feature data of the at least one first sample into the second model so that the second model separately outputs multiple first output values each based on feature data of a first sample of the at least one first sample, and obtaining a first training sample set from the at least one first sample based on the first output values separately output by the second model, a first output value being used to determine whether a corresponding first sample is selected as a training sample of the first training sample set, where the first training set is for training the first model.

Abstract: This disclosure is related to determining an item push list for a user based on a reinforcement learning model. In one aspect, a method includes obtaining M first item lists that have been predetermined for a first user. Each first item list includes i?1 items. For each first item list, an ith state feature vector is obtained. The ith state feature vector includes a static feature and a dynamic feature. The ith state feature vector is provided as input to the reinforcement machine learning model. The reinforcement model outputs a weight vector including weights of sorting features. A sorting feature vector of each item in a candidate item set corresponding to the first item list is obtained. The sorting feature vector includes feature values of sorting features. M updated item lists are determined for the first item lists based on a score for each item in M candidate item sets.

Abstract: This disclosure is related to determining an item push list for a user based on a reinforcement learning model. In one aspect, a method includes obtaining M first item lists that have been predetermined for a first user. Each first item list includes i-1 items. For each first item list, an ith state feature vector is obtained. The ith state feature vector includes a static feature and a dynamic feature. The ith state feature vector is provided as input to the reinforcement machine learning model. The reinforcement model outputs a weight vector including weights of sorting features. A sorting feature vector of each item in a candidate item set corresponding to the first item list is obtained. The sorting feature vector includes feature values of sorting features. M updated item lists are determined for the first item lists based on a score for each item in M candidate item sets.