Abstract: Provided are a target retrieval method and device, and a storage medium.

Abstract: The three-state spintronic device includes: a bottom electrode, a magnetic tunnel junction and a top electrode from bottom to top. The magnetic tunnel junction includes: a spin-orbit coupling layer, a ferromagnetic free layer, a barrier tunneling layer, a ferromagnetic reference layer, three local magnetic domain wall pinning centers and domain wall nucleation centers. An antisymmetric exchange interaction is modulated, and the magnetic domain wall pinning centers are embedded in an interface between a heavy metal and the ferromagnetic free layer. The magnetic domain wall nucleation centers are at two ends of the ferromagnetic free layer. A current pulse flows through the spin-orbit coupling layer to generate a spin current and the spin current is injected into the ferromagnetic free layer. Under a control of all-electrical controlled, an effective field of a spin-orbit torque drives domain wall to move and displace.

Abstract: The present application discloses a method for improving density-tolerant yield of maize and use thereof, belongs to the field of plant biotechnology breeding. Compared with the Ig1 mutant strain, the LG1/Ig1 heterozygous genotype strain has excellent agronomic characters such as a widened leaf, a larger spike, a reduced empty stalk ratio and/or an increased yield by close planting; compared with the LG1 wild-type strain, the LG1/Ig1 heterozygous genotype material reduces a leaf angle and tassel branch number of maize, and has the effect of increasing the yield by close planting. The method has a wide application prospect in the field of maize high-yield breeding.

Abstract: A method for obtaining a cover image includes: obtaining a plurality of first cropped images of an original image corresponding to a candidate resource; obtaining an aesthetic score of each of the plurality of first cropped images; and determining a target cover image of the candidate resource from the plurality of first cropped images based on the aesthetic score of each first cropped image.

Abstract: The present invention provides a preparation method for a carotenoid preparation having high bioavailability and high stability, comprising the following steps: a) dissolving part of a water-soluble colloid and a filling substance in water to form an aqueous phase; b) adding a carotenoid crystal to the aqueous phase and stirring for dispersion so as to form a dispersion liquid; c) loading the dispersion liquid into a first-stage grinding machine for first grinding to form a first-stage grinding liquid; d) adding the remaining water-soluble colloid and filling substance to the first-stage grinding liquid, loading same into a second grinding machine for secondary grinding to obtain a second-stage grinding liquid; and e) drying the moisture in the second-stage grinding liquid to obtain carotenoid dry powder or particles.

Abstract: An application-level memory control group of a first application may be created when the first application is opened. An anonymous page of the first application is added to a least recently used linked list of the application-level memory control group, and a file page of the first application is added to a global least recently used linked list. An application-level memory control group is created in a dimension of an application, and an anonymous page of the application is managed in a refined manner. In addition, a file page of the application-level memory control group may be managed based on a global least recently used linked list.

Abstract: The present invention provides a method to prepare polymer materials with interlocked porous structures by freezing and demulsification, which includes: (1) Preparing an emulsion containing uncrosslinked polymers and crosslinking agents. The uncrosslinked polymers are presented in the organic phase, and the crosslinking agents are presented in the organic phase or water phase. Under freezing, the demulsification is occurred which leads to the interaction between polymers and crosslinking agents, and the crosslinked materials are obtained. (2) After removing the ice crystals, polymer materials with interlocked porous structures are synthesized. The method provided by the present invention is simple to operate, and can well adjust the porous structures of obtained porous polymer materials. In addition, it is suitable for large scale manufacturing. At the same time, this process can form different functional porous polymer materials by simply changing the used monomers.

Abstract: An operation method and apparatus for a network layer in a Deep Neural Network are provided. The method includes: acquiring a weighted tensor of the network layer in the Deep Neural Network, the weighted tensor comprising a plurality of filters; converting each filter into a linear combination of a plurality of fixed-point convolution kernels by splitting the filter, wherein a weight value of each of the fixed-point convolution kernels is a fixed-point quantized value having a specified bit-width; for each filter, performing a convolution operation on input data of the network layer and each of the fixed-point convolution kernels, respectively, to obtain a plurality of convolution results, and calculating a weighted sum of the obtained convolution results based on the linear combination of the plurality of fixed-point convolution kernels of the filter to obtain an operation result of the filter; determining output data of the network layer.

Abstract: Provided are a method and an apparatus for generating prediction information, an electronic device, and a computer readable medium. The method includes: generating, based on first user characteristic information of a target user, anonymous user information of the target user (201); sending the anonymous user information to a second processing end to enable the second processing end to generate prediction information based on the anonymous user information and second user characteristic information (202) of the target user. Data interaction and sharing are realized while ensuring data privacy, thereby improving accuracy of the prediction information.

Abstract: In certain aspects, a three-dimensional (3D) memory device includes a stack structure including a core region and a staircase region, a channel structure extending through the stack structure in the core region, and a first support structure extending through the stack structure in the staircase region. The first support structure includes a first portion extending along a first direction and a second portion protruding from the first portion along a second direction perpendicular to the first direction. The first support structure is located between two blocks of the 3D memory device.

Abstract: Disclosed are a hexavalent norovirus VLP vaccine formulation and a preparation method thereof. The formulation comprises six proteins having amino acid sequences shown in SEQ ID No. 2, SEQ ID No. 4, SEQ ID No. 6, SEQ ID No. 8, SEQ ID No. 10, and SEQ ID No. 12. A Pichia pastoris expression system was used to develop the hexavalent norovirus vaccine. Sequencing, digestion, and exogenous gene expression detection were conducted to identify recombinant vectors and select high-expression strains. VLPs were purified by sucrose density gradient centrifugation and identified by electron microscopy, with the results demonstrating that the obtained candidate hexavalent vaccine VLPs had a purity of over 95% and each VLP was intact in structure and uniform in size, indicating structural integrity and stable performance of the VLPs. The hexavalent VLP vaccine induced long-lasting and high-titer antibody responses and generated specific blocking antibodies and T cell immune responses.

Abstract: A deep neural network training method and apparatus and a computer device are provided. The deep neural network training method includes: obtaining task attributes of nodes in a current network layer in a tree-like network topology (S101); performing cluster analysis on the nodes in the current network layer based on the task attributes of the nodes in the current network layer and extracting a common part of task attributes of multiple nodes in a same category as a task attribute for a parent node of the multiple nodes (S102); training a network parameter of each parent node based on a task attribute of this parent node (S103); and determining that training of a deep neural network corresponding to the tree-like network topology is completed, after completion of training of all nodes in all network layers (S104). The operation efficiency of deep learning can be improved through this solution.

Abstract: The embodiments of the present disclosure provides a method and an apparatus for training a neural network model, A training sample is obtained, and the neural network model is trained using the training sample. When the neural network model is trained, power exponential domain fixed-point encoding is performed on a first activation inputted into each network layer and a network weight of each network layer, and an encoded first activation and an encoded network weight are power exponential domain fixed-point data, which when used in the operation, can cause a matrix multiplication operation involved to be converted into an addition operation in the power exponential domain by means of the power exponential domain encoding. The hardware resources required for the addition operation are significantly less than that required for the multiplication operation, which therefore can greatly reduce the hardware resource overhead required for running the neural network model.

Abstract: Embodiments of the present application provide a data storage method, data acquisition method and device thereof. The method includes allocating an N-dimensional first parameter vector for N pieces of to-be-stored data; performing N-dimensional permutation on the first parameter vector, to obtain N second parameter vectors each having N dimensions; constructing a neural network model that maps the current second parameter vectors to expected data samples of the N pieces of to-be-stored data; adjusting model parameters of the neural network model and/or the first parameter vector until expected data samples of the N pieces of to-be-stored data regress to the N pieces of to-be-stored data, the expected data samples being obtained from the current second parameter vectors based on the trained neural network model; storing the current first parameter vector.

Abstract: Embodiments of methods and apparatuses for object detection and of computer devices are disclosed.

Abstract: Embodiments of the present application provide a method, apparatus, and electronic device for eliminating distortion of a distorted image. Side information components of a distorted image are generated. The distorted image is resulted from image processing on an original image. Side information components represent distortion features of the distorted image with respect to original image. Distorted image color components of the distorted image and the side information components are input into a pre-established convolutional neural network model for convolution filtering to obtain distortion-eliminated image color components. The convolutional neural network model is obtained through training based on a preset training set. The training set comprises original sample images, distorted image color components of multiple distorted images corresponding to each of the original sample images, and side information components of each of the distorted images.

Abstract: A deep neural network operation method and apparatus are provided. The method comprises: obtaining an input feature map of a network layer; displacing respectively, according to a preset displacement parameter, each of channels of the input feature map of the network layer along axes, to obtain a displaced feature map, wherein the preset displacement parameter comprises displacement amounts of the channel in the axes; and performing convolution operation on the displaced feature map with a 1×1 convolution kernel to obtain an output feature map of the network layer. The operation efficiency of the DNN can be improved through the above method.

Abstract: The embodiments of the present application provide an image segmentation method, an image segmentation apparatus, and a fully convolutional network system. The method includes: acquiring a target image to be processed; acquiring image feature data of the target image; inputting the image feature data into a pre-trained target network far image segmentation to obtain an output; wherein the target network is a fully convolutional network comprising a hybrid context network structure, and the hybrid context network structure is configured to extract a plurality of reference features at a predetermined scale and fuse them into a target feature that matches a scale of a target object in a segmented image; and wherein the target network is trained with sample images containing target objects at different scales; and obtaining an image segmentation result for the target image based on the output.

Abstract: An operation method and apparatus for a network layer in a Deep Neural Network are provided.