Abstract: The disclosure discloses a conversation-based recommending method. A directed graph corresponding to a current conversation is obtained. The current conversation includes clicked items, the directed graph includes nodes and directed edges between the nodes, each node corresponds to a clicked item, and each directed edge indicates relationship data between the nodes. For each node of the directed graph, an attention weight is determined for each directed edge corresponding to the node based on a feature vector of the node and the relationship data for each node of the directed graph. A new feature vector of the node is determined based on the relationship data and the attention weight of each directed edge. A feature vector of the current conversation is determined based on the new feature vector of each node. An item is recommended based on the feature vector of the current conversation.

Abstract: A peony PoWOX11 gene and an application of a coded protein thereof are provided in the present disclosure, belonging to the field of biotechnology. The peony PoWOX11 gene is introduced into Arabidopsis thaliana to obtain a transgenic Arabidopsis thaliana plant stably expressing peony PoWOX11 gene, with bolting and flowering of the transgenic Arabidopsis thaliana plant delayed; where the peony PoWOX11 gene has a nucleotide sequence as shown in SEQ ID NO. 7, and a protein coded by the peony PoWOX11 gene has an amino acid sequence as shown in SEQ ID NO. 8.

Abstract: Disclosed are a peony PoWOX4 gene and applications of a coded protein thereof, belonging to the field of biotechnology. According to the present disclosure, Arabidopsis thaliana is taken as a model plant, and the peony PoWOX4 gene is transformed into Arabidopsis thaliana to promote the early bolting, flowering and vegetative growth of Arabidopsis thaliana.

Abstract: A method and an apparatus for generating a shared encoder are provided, which belongs to a field of computer technology and deep learning. The method includes: sending by a master node a shared encoder training instruction to child nodes, so that each child node obtains training samples based on a type of a target shared encoder included in the training instruction; sending an initial parameter set of the target shared encoder to be trained to each child node after obtaining a confirmation message returned by each child node; obtaining an updated parameter set of the target shared encoder returned by each child node; determining a target parameter set corresponding to the target shared encoder based on a first preset rule and the updated parameter set of the target shared encoder returned by each child node.

Abstract: Provided are a text processing method, a device and a storage medium, relating to a field of computer technology, and especially to a field of artificial intelligence, such as natural language processing and deep learning. The specific implementation scheme includes: performing text processing on first text, by using a text processing acceleration operator; and processing, in parallel and faster, content after the text processing, by using the text processing acceleration operator. Text processing and parallel acceleration are carried out by the text processing acceleration operator, which can improve the speed of text processing.

Abstract: A data processing method and apparatus based on a recurrent neural network and a device are provided. The recurrent neural network includes multiple recurrent units, each recurrent unit includes multiple data processing nodes and a start node, at least one recurrent unit includes an end node, and at least one data processing node is included between the start node and the end node. During the processing of the first target processing object in a first recurrent unit, in a case that the first target processing object does not satisfy the first preset condition, the start node in the first recurrent unit is run to add a tag to the data processing nodes subsequent to the start node and stop addition of the tag in response to reaching the end node, and no processing is performed by the data processing nodes with the tag.

Abstract: An operator processing method of a deep learning framework an electronic device, and a storage medium are provided, which relate to a field of computer technology, especially in a field of artificial intelligence technology such as deep learning. The specific implementation scheme includes: acquiring an operator to be processed, where the operator to be processed includes a template parameter independent of the deep learning framework and an operator kernel function; parsing, in response to receiving an input information for the operator to be processed, the template parameter by using the input information to obtain a plurality of complete template parameters related to the deep learning framework; and processing the operator kernel function according to the plurality of complete template parameters, to obtain an available operator for the deep learning framework.

Abstract: The present disclosure provides an image recognition method and apparatus, an electronic device and a readable storage medium, and relates to the field of artificial intelligence technologies, such as image processing and deep learning technologies. The image recognition method includes: acquiring a to-be-recognized image, and determining a to-be-recognized subject in the to-be-recognized image; extracting a subject feature of the to-be-recognized subject, and obtaining a target feature according to the subject feature; determining a target candidate feature in a plurality of candidate features using the target feature; and taking a class corresponding to the target candidate feature as a recognition result of the to-be-recognized subject. With the present disclosure, different image recognition requirements may be met, and a speed and accuracy of image recognition may be improved.

Abstract: A method and apparatus is provided for generating and applying a deep learning model based on a deep learning framework, and relates to the field of computers. A specific implementation solution includes that a basic operating environment is established on a target device, where the basic operating environment is used for providing environment preparation for an overall generation process of a deep learning model; a basic function of the deep learning model is generated in the basic operating environment according to at least one of a service requirement and a hardware requirement, to obtain a first processing result; an extended function of the deep learning model is generated in the basic operating environment based on the first processing result, to obtain a second processing result; and a preset test script is used to perform function test on the second processing result, to output a test result.

Abstract: The disclosure provides a method for processing data, and an electronic device. The method includes: obtaining first attribute information of input data and second attribute information of a computing device corresponding to the input data; selecting a target operator implementation mode from a plurality of candidate operator implementation modes based on the first attribute information and the second attribute information; determining a plurality of sub-operators included in an operator required for the input data from an operator library based on the target operator implementation mode, to generate the operator; and obtaining an operation result by performing an operation on the input data by the computing device based on the operator.

Abstract: The present disclosure discloses a tobacco leaf foliar spraying substance for reducing harmful ingredients in cheroot, a method for reducing carcinogenic components in flue-cured tobacco leaves and flue-cured tobacco shreds. The tobacco leaf foliar spraying substance contains a lotus leaf extract, the harmful chemical ingredients comprise N-nitrosonornicotine, 4-(N-methyl-nitrosamine)-1-(3-pyridinyl)-1-butanone, N-nitrosoanabasine and N-nitrosoanatabine. Foliar spraying is performed on a fertile field by using the lotus leaf extract before tobacco leaves are harvested and modulated, which not only significantly promotesagronomic characters and economic traits of tobacco and alleviates tobacco leaf browning but also effectively reduces harmful chemical ingredients unique to tobacco leaves, such as nitrosamine and nicotine.

Abstract: Embodiments of the present disclosure disclose a method for processing tasks in parallel, a device and a storage medium, and relate to a field of artificial intelligent technologies. The method includes: determining at least one parallel computing graph of a target task; determining a parallel computing graph and an operator scheduling scheme based on a hardware execution cost of each operator task of each of the at least one parallel computing graph in a cluster, in which the cluster includes a plurality of nodes for executing the plurality of operator tasks, and each parallel computing graph corresponds to at least one operator scheduling scheme; and scheduling and executing the plurality of operator tasks of the determined parallel computing graph in the cluster based on the determined parallel computing graph and the determined operator scheduling scheme.

Abstract: The disclosed system may include a support structure dimensioned for a user's hand. The system may also include transmitting electrodes coupled to a first finger portion of the support structure and may further include receiving electrodes coupled to a second, different finger portion of the support structure. The system may also include a controller that is coupled to the support structure and that is communicatively connected to the transmitting and receiving electrodes. The controller may also be configured to cause the transmitting electrodes to transmit a signal, detect at least some of the transmitted signal at the receiving electrodes and, based on the detected signal, determine that at least two fingers of the user's hand are touching each other. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The present disclosure provides a method for generating a backbone network, an apparatus for generating a backbone network, a device, and a storage medium. The method includes: acquiring a set of a training image, a set of an inference image, and a set of an initial backbone network; training and inferring, for each initial backbone network in the set of the initial backbone network, the initial backbone network by using the set of the training image and the set of the inference image, to obtain an inference time and an inference accuracy of a trained backbone network in an inference process; determining a basic backbone network based on the inference time and the inference accuracy of the trained backbone network in the inference process; and obtaining a target backbone network based on the basic backbone network and a preset target network.

Abstract: Provided is a lightweight model training method, an image processing method, a device and a medium. The lightweight model training method includes: acquiring first and second augmentation probabilities and a target weight adopted in an e-th iteration; performing data augmentation on a data set based on the first and second augmentation probabilities respectively, to obtain first and second data sets; obtaining a first output value of a student model and a second output value of a teacher model based on the first data set; obtaining a third output value and a fourth output value based on the second data set; determining a distillation loss function, a truth-value loss function and a target loss function; training the student model based on the target loss function; and determining a first augmentation probability or target weight to be adopted in an (e+1)-th iteration in a case of e is less than E.

Abstract: A high internal phase emulsion stabilized by a low content of surfactant and its preparation method thereof are provided. An oil ethoxylate is added to a vegetable oil, and the resulting mixture is uniformly mixed under a low-speed stirring at room temperature. Water is added dropwise to the mixture of surfactant and vegetable oil under stirring, and a homogenization is performed on the obtained dispersed system by a high-shear dispersion emulsification homogenizer to obtain the high internal phase emulsion stabilized by oil ethoxylate. The preparation method requires a low content of surfactant, mild preparation conditions and simple operations. When the oil-phase volume fraction is 83 vol %, the minimum mass fraction of the oil ethoxylate to stabilize the high internal phase emulsion is 0.6 wt %, and the prepared oil-in-water type high internal phase emulsion has excellent stability and shows bright color, delicate odor, hydra feel and being easy to apply.

Abstract: Technologies are generally described for dynamic optimization of environmental controls. A system according to some examples may determine a person's environmental preferences and generate a time-based environmental control model for that person. The system may also generate a location model defining current and predicted future locations for the person. The environmental model may take into account person's previous or current activity and be adjusted based on sensed data from interior and exterior environmental sensors and body sensors on the person. Models for multiple people may be combined based on priorities or personal characteristics. Models may also be stored in mobile devices such that settings can be implemented in future locations prior to arrival of the person. Implemented settings may be adjusted for additional people as they arrive at the location.

Abstract: The present disclosure discloses a tobacco leaf foliar spraying substance for reducing harmful ingredients in cheroot, a method for reducing carcinogenic components in flue-cured tobacco leaves and flue-cured tobacco shreds. The tobacco leaf foliar spraying substance contains a lotus leaf extract, the harmful chemical ingredients comprise N-nitrosonornicotine, 4-(N-methyl-nitrosamine)-1-(3-pyridinyl)-1-butanone, N-nitrosoanabasine and N-nitrosoanatabine. Foliar spraying is performed on a fertile field by using the lotus leaf extract before tobacco leaves are harvested and modulated, which not only significantly promotesagronomic characters and economic traits of tobacco and alleviates tobacco leaf browning but also effectively reduces harmful chemical ingredients unique to tobacco leaves, such as nitrosamine and nicotine.

Abstract: The present disclosure provides a method for training a feature extraction model, a method for classifying an image and related apparatuses, and relates to the field of artificial intelligence technology such as deep learning and image recognition. The scheme comprises: extracting an image feature of each sample image in a sample image set using a basic feature extraction module of an initial feature extraction model, to obtain an initial feature vector set; performing normalization processing on each initial feature vector in the initial feature vector set using a normalization processing module of the initial feature extraction model, to obtain each normalized feature vector; and guiding training for the initial feature extraction model through a preset high discriminative loss function, to obtain a target feature extraction model as a training result.

Abstract: A resource allocation method, including: determining a neural network model to be allocated resources, and determining a set of devices capable of providing resources for the neural network model; determining, based on the set of devices and the neural network model, first set of evaluation points including first number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme; updating and iterating first set of evaluation points to obtain second set of evaluation points including second number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme, and second number being greater than first number; and selecting a resource allocation scheme with minimum resource use cost from the second set of evaluation points as a resource allocation scheme for allocating resources to the neural network model.