Abstract: In one embodiment, there is provided an apparatus for denoising a medical image. The apparatus includes a denoising artificial neural network (ANN) configured to denoise input image data. The denoising ANN is trained, based at least in part, on at least one loss function. The at least one loss function includes a task-oriented loss.

Abstract: A feature set determining method includes obtaining, according to a received feature set determining request, data used for feature learning. The feature set determining request includes a learning objective of the feature learning. The method includes performing type analysis on the data to divide the data into first-type data and second-type data. The method includes performing semi-supervised learning on the first-type data to extract multiple first-type features. The method includes performing adaptive learning on the second-type data to extract multiple second-type features. The method includes evaluating the first-type features and the second-type features to obtain an optimal feature set.

Abstract: A data processing method is disclosed, and the method includes: encoding a data chunk of a predetermined size, to generate an error-correcting data chunk corresponding to the data chunk, where the data chunk includes a data object, and the data object includes a key, a value, and metadata; and generating a data chunk index and a data object index, where the data chunk index is used to retrieve the data chunk and the error-correcting data chunk corresponding to the data chunk, the data object index is used to retrieve the data object in the data chunk, and each data object index is used to retrieve a unique data object.

Abstract: Soybean plant and seed comprising soybean transgenic event SHZD32-01 and DNA molecules unique to the event. Also provided are use of the plant parts, seeds; the soybean transgenic event SHZD32-01 comprises at least one of the nucleic acid molecules of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 9, and their complete complementary sequences. The method of use include the method for producing soybean with tolerance to herbicide glyphosate, producing a soybean-based commercial product, and controlling weeds in a field comprising soybean plants. Soybean strains comprising the soybean event SHZD32-01 exhibits strong tolerance to glyphosate and is helpful for weeds control. DNA detection of the soybean event SHZD32-01 is useful for identifying the soybean event SHZD32-01 in a sample and may be applied to methods for breeding soybean plants comprising the DNA.

Abstract: A data processing method is disclosed, and the method includes: encoding a data chunk of a predetermined size, to generate an error-correcting data chunk corresponding to the data chunk, where the data chunk includes a data object, and the data object includes a key, a value, and metadata; and generating a data chunk index and a data object index, where the data chunk index is used to retrieve the data chunk and the error-correcting data chunk corresponding to the data chunk, the data object index is used to retrieve the data object in the data chunk, and each data object index is used to retrieve a unique data object.

Abstract: Soybean plant and seed comprising soybean transgenic event SHZD32-01 and DNA molecules unique to the event. Also provided are use of the plant parts, seeds; the soybean transgenic event SHZD32-01 comprises at least one of the nucleic acid molecules of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 9, and their complete complementary sequences. The method of use include the method for producing soybean with tolerance to herbicide glyphosate, producing a soybean-based commercial product, and controlling weeds in a field comprising soybean plants. Soybean strains comprising the soybean event SHZD32-01 exhibits strong tolerance to glyphosate and is helpful for weeds control. DNA detection of the soybean event SHZD32-01 is useful for identifying the soybean event SHZD32-01 in a sample and may be applied to methods for breeding soybean plants comprising the DNA.

Abstract: A feature set determining method includes obtaining, according to a received feature set determining request, data used for feature learning. The feature set determining request includes a learning objective of the feature learning. The method includes performing type analysis on the data to divide the data into first-type data and second-type data. The method includes performing semi-supervised learning on the first-type data to extract multiple first-type features. The method includes performing adaptive learning on the second-type data to extract multiple second-type features. The method includes evaluating the first-type features and the second-type features to obtain an optimal feature set.