Abstract: Provided is a liquid crystal display panel. The liquid crystal display panel includes: a first substrate and a second substrate that are opposite to each other, and a liquid crystal layer between the first substrate and the second substrate. The liquid crystal display panel includes a plurality of sub-pixel regions. Each of the plurality of sub-pixel regions includes 4N sub-pixel sub-regions arranged in a single column, and the 4N sub-pixel sub-regions are organized into one group, or a plurality of groups that are adjacent to each other. A same group of sub-pixel sub-regions includes two first sub-regions and two second sub-regions. N is an integer greater than or equal to 1.

Abstract: A machine learning model is trained using original source domain data through empirical risk minimization and a model sensitivity map is computed. Each sensitive frequency point on the model sensitivity map is targeted. An adversarial technique is employed to generate spectral adversarial images based on the model sensitivity map and an image amplitude spectrum is augmented. The generated spectral adversarial images are mixed with the original source domain data to finetune the machine learning model and deployment of the finetuned machine learning model is facilitated.

Abstract: Disclosed are a method, a device and a computer-readable storage medium for registration and login. The method includes: receiving, on a registration device side, registration information used to register a first object; performing a registration operation corresponding to the first object according to the registration information; sending the registration information to a login device that subscribes to the registration information in advance after the registration operation is successful; receiving, on a login device side, the registration information used to successfully register the first object sent by a registration device; receiving login information used to login a second object; and performing a login operation corresponding to the second object if it is determined that the login information has not been used to register the second object and the login information matches the registration information sent by the registration device.

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.