Abstract: A method for processing batch-normalized data, an electronic device, and a storage medium are provided. The method includes: a plurality of sample data are input into a batch normalization (BN) layer in a target network to be trained for normalization to obtain a processing result of the BN layer, wherein the plurality of sample data are obtained by extracting features of a plurality of image data; a shift adjustment of initial BN is performed on the processing result of the BN layer according to a specified constant shift to obtain a processing result of a post-shifted BN layer; and the processing result of the post-shifted BN layer is nonlinearly mapped through a rectified linear unit (ReLU) of an activation layer, a loss function is obtained step by step and then back propagation is carried out to obtain a first target network.

Abstract: A firearm system includes a firearm and a user identification system built-in with the firearm. The user identification system includes a locking module that releasably locks up a trigger-actuated firing mechanism of the firearm from being actuated, a camera module being activated for generating tangible evidence of operation incidents, and an activation module operatively linked to the locking module. The activation module pre-stores authorized user data and continuously collects user operation data not only to activate the camera module for capturing image but also to actuate the locking module to unlock the trigger-actuated firing mechanism when the authorized user data is matched with the user operation data. Once the authorized user data is not match with the user operation data, the trigger-actuated firing mechanism is instantaneously locked to prevent an unauthorized usage of the firearm.

Abstract: Embodiments of the present disclosure disclose normalization methods and apparatuses for a deep neural network, devices, and storage media. The method includes: inputting an input data set into a deep neural network, the input data set including at least one piece of input data; normalizing a feature map set output by means of a network layer in the deep neural network from at least one dimension to obtain at least one dimension variance and at least one dimension mean; and determining a normalized target feature map set based on the at least one dimension variance and the at least one dimension mean. Based on the embodiments of the present disclosure, normalization is performed along at least one dimension so that statistics information of each dimension of a normalization operation is covered, thereby ensuring good robustness of statistics in each dimension without excessively depending on the batch size.

Abstract: Provided are methods for inducing apoptosis of cells using quinolinol compounds. Combinations of quinolinol compounds can be used in the methods. Combinations of one or more quinolinol compound and one or more inhibitors of cell growth arrest, e.g., Nutlin3a, can also be used in the methods. Also provided are compositions comprising of one or more quinolinol compound and one or more pharmaceutically acceptable carrier. The compositions can comprise one or more quinolinol compound, one or more pharmaceutically acceptable carrier, and one or more inhibitors of cell growth arrest, e.g., Nutlin3a.

Abstract: Provided are methods for inducing apoptosis of cells using quinolinol compounds. Combinations of quinolinol compounds can be used in the methods. Combinations of one or more quinolinol compound and one or more inhibitors of cell growth arrest, e.g., Nutlin3a, can also be used in the methods. Also provided are compositions comprising of one or more quinolinol compound and one or more pharmaceutically acceptable carrier. The compositions can comprise one or more quinolinol compound, one or more pharmaceutically acceptable carrier, and one or more inhibitors of cell growth arrest, e.g., Nutlin3a.