Abstract: A computer-implemented method is provided for disentangled data generation. The method includes accessing, by a bidirectional Long Short-Term Memory (LSTM) with a multi-head attention mechanism, a dataset including a plurality of pairs each formed from a given one of a plurality of input text structures and given one of a plurality of style labels for the plurality of input text structures. The method further includes training the bidirectional LSTM as an encoder to disentangle a sequential text input into disentangled representations comprising a content embedding and a style embedding based on a subset of the dataset. The method also includes training a unidirectional LSTM as a decoder to generate a next text structure prediction for the sequential text input based on previously generated text structure information and a current word, from a disentangled representation with the content embedding and the style embedding.

Abstract: A basic unit for a power converter, a power converter, and a universal power interface are disclosed. The basic unit includes an inductor, a power half-bridge, a first terminal, a second terminal, a third terminal, and a fourth terminal, where an end of the inductor is connected to a midpoint of the power half-bridge, and the other end of the inductor is connected to the first terminal; a source terminal of a lower bridge arm of the power half-bridge is connected to the second terminal and the fourth terminal; and a drain terminal of an upper bridge arm of the power half-bridge is connected to the third terminal. The manufacturing costs of a microgrid system and the difficulty of later maintenance can be reduced.

Abstract: A computer-implemented method is provided for disentangled data generation. The method includes accessing, by a bidirectional Long Short-Term Memory (LSTM) with a multi-head attention mechanism, a dataset including a plurality of pairs each formed from a given one of a plurality of input text structures and given one of a plurality of style labels for the plurality of input text structures. The method further includes training the bidirectional LSTM as an encoder to disentangle a sequential text input into disentangled representations comprising a content embedding and a style embedding based on a subset of the dataset. The method also includes training a unidirectional LSTM as a decoder to generate a next text structure prediction for the sequential text input based on previously generated text structure information and a current word, from a disentangled representation with the content embedding and the style embedding.