Abstract: This application provides a non-aqueous electrolyte and a preparation method thereof, and a secondary battery and an electric apparatus containing the same. The non-aqueous electrolyte contains the non-aqueous solvent and lithium ions, first cations, and first anions dissolved therein, where the first cation is a metal cation Men+ other than the lithium ion, n representing a chemical valence of the metal cation; the first anion is a tetrafluoroborate anion BF4?; mass concentration of the first cations in the non-aqueous electrolyte is D1 ppm, and mass concentration of the first anions in the non-aqueous electrolyte is D2 ppm, both based on total mass of the non-aqueous electrolyte; and the non-aqueous electrolyte satisfies that D1 is 0.1 to 1250 and that D1/D2 is 0.02 to 2. The non-aqueous electrolyte in this application enables the secondary battery to have good cycling performance, safety performance, and kinetic performance.

Abstract: An online system actively and randomly selects content items to be labeled for training a classifier. An online system receives content items from client devices of users and selects sets of the content items to be labeled by human labelers. The randomly selected content items are selected at random from the received content items, and the actively selected content items are selected based on the classifier's confidence in accurately predicting the classification of the content items. The online system may use a histogram of content items to actively select content items. The online system assigns the content items to bins of the histogram based on priority scores and selects content items with priority scores of the highest percentile. The online system provides the selected content items to human labelers for labeling. The labeled content items are then used for training the classifier.

Abstract: An online system predicts topics for content items. The online system provides one or more topic labels for a user to apply concurrently while a user is composing a post, in response to requests periodically received from the user's device. A request includes information such as content composed by the user and contextual information. The online system employs machine learning techniques to analyze content composed by a user and contextual information thereby to predict topic labels. Different machine learning models for classifying individual topic labels, identifying relevant topic labels, and/or detecting changes in existing topic predictions are developed. Some machine learning models predict topics for full content and some predict topics for partial content. The online system trains the machine learning models to ensure accurate topic predictions are provided timely. The online system employs various machine learning model training methods such as active training and gradient training.

Abstract: An online system actively and randomly selects content items to be labeled for training a classifier. An online system receives content items from client devices of users and selects sets of the content items to be labeled by human labelers. The randomly selected content items are selected at random from the received content items, and the actively selected content items are selected based on the classifier's confidence in accurately predicting the classification of the content items. The online system may use a histogram of content items to actively select content items. The online system assigns the content items to bins of the histogram based on priority scores and selects content items with priority scores of the highest percentile. The online system provides the selected content items to human labelers for labeling. The labeled content items are then used for training the classifier.

Abstract: An online system predicts topics for content items. The online system provides one or more topic labels for a user to apply concurrently while a user is composing a post, in response to requests periodically received from the user's device. A request includes information such as content composed by the user and contextual information. The online system employs machine learning techniques to analyze content composed by a user and contextual information thereby to predict topic labels. Different machine learning models for classifying individual topic labels, identifying relevant topic labels, and/or detecting changes in existing topic predictions are developed. Some machine learning models predict topics for full content and some predict topics for partial content. The online system trains the machine learning models to ensure accurate topic predictions are provided timely. The online system employs various machine learning model training methods such as active training and gradient training.

Abstract: The presently disclosed subject matter provides methods and compositions for modulating gene expression in myocytes. Also provided are cells comprising the compositions of the presently disclosed subject matter.

Abstract: The presently disclosed subject matter provides methods and compositions for modulating gene expression in myocytes. Also provided are cells comprising the compositions of the presently disclosed subject matter.

Abstract: The presently disclosed subject matter provides methods and compositions for modulating gene expression in myocytes. Also provided are cells comprising the compositions of the presently disclosed subject matter.