Abstract: Provided are a lithium-ion battery positive electrode plate, comprising a positive electrode film layer disposed on at least one surface of the positive electrode current collector, wherein the positive electrode film layer comprises a positive electrode active material mixture composed of the following materials: polycrystalline particles of a first positive electrode active material with a particle size of 11.0-20.0 ?m; polycrystalline particles of a second positive electrode active material with a particle size of 6.0-10.5 ?m; and monocrystalline particles of a third positive electrode active material with a particle size of 1.1-5.2 ?m; wherein the number of the polycrystalline particles of the first positive electrode active material is a, the number of the polycrystalline particles of the second positive electrode active material is b, the number of the monocrystalline particles of the third positive electrode active material is c, and (a+b):c is in the range of 5.7:4.3 to 7.7:2.3.

Abstract: A computer-implemented method for meta-learning is provided. The method includes receiving a training time series and labels corresponding to some of the training time series. The method further includes optimizing time series augmentations of the training time series using a time series augmentation selection process performed by a meta learner to obtain a selected augmentation from a plurality of candidate augmentations. The method also includes training a time series encoder with contrastive loss using the selected augmentation to obtain a learned time series encoder. The method additionally includes learning, by the learned time series encoder, a vector representation of another time series. The method further includes performing, by the learned time series encoder, a downstream task of label classification for at least a portion of the other time series.

Abstract: A computer-implemented method is provided for keyphrase generation. The method includes pretraining, by a processor device, a policy neural network on training documents using a sequence-to-sequence model. The training documents are each associated with a list of keyphrases included therein. The method further includes training, by the processor device, the policy neural network using reinforcement learning with a summarization reward on present annotated keyphrases in an input training document and absent annotated keyphrase from the input training document that semantically describe a concept of the input training document. The method also includes predicting, by the processor device, new keyphrases using the trained policy neural network.

Abstract: Systems and methods for augmenting data sets is provided. The systems and methods includes feeding an original document into a data augmentation generator to produce one or more augmented documents; calculating a contrastive loss between the original document and the one or more augmented documents; and using the original document and the one or more augmented documents to train a neural network.

Abstract: The present application relates to a secondary battery and a battery module, a battery pack and an apparatus containing the secondary battery. In particular, the secondary battery includes a positive electrode plate, a negative electrode plate, a separator and an electrolyte, characterized in that: the secondary battery satisfies the following formula I: 2?|(AF/AE?1)/(CE/CF?1)|?7 (Formula I).