Abstract: The embodiments of the present disclosure disclose a system and method. The system may include at least one storage device configured to storage computer instruction; and at least one processor, in communication with the storage device. When executing the computer instructions, the at least one processor is configured to direct the system to perform operations including: obtaining a sensing signal of at least one sensing device; identifying a signal feature of the sensing signal; and determining, based on the signal feature, an operation of a target object associated with the at least one sensing device.

Abstract: The present disclosure provides a microphone including at least one acoustoelectric transducer and an acoustic structure. The acoustoelectric transducer is configured to convert a sound signal to an electrical signal. The acoustic structure includes a sound guiding tube and an acoustic cavity. The acoustic cavity is in acoustic communication with the acoustoelectric transducer, and is in acoustic communication with outside of the microphone through the sound guiding tube. The acoustic structure has a first resonance frequency, the acoustoelectric transducer has a second resonance frequency, and an absolute value of a difference between the first resonance frequency and the second resonance frequency is not less than 100 Hz. By disposing different acoustic structures, resonance peaks in different frequency ranges may be added to the microphone, which improves a sensitivity of the microphone near multiple resonance peaks, thereby improving a sensitivity of the microphone in the entire wide frequency band.

Abstract: A method for training a Neural Network (NN) model for imitating demonstrator's behavior. The method includes: obtaining demonstration data representing the demonstrator's behavior for performing a task, the demonstration data includes state data, action data and option data, wherein the state data correspond to a condition for performing the task, the option data correspond to subtasks of the task, and the action data correspond to the demonstrator's actions performed for the task; sampling learner data representing the NN model's behavior for performing the task based on a current learned policy; and updating the policy by using a generative adversarial imitation learning (GAIL) process based on the demonstration data and the learner data.

Abstract: A compound property prediction method is provided for an electronic device. The method includes obtaining chemical structure information of a target compound, the chemical structure information including an atom and a chemical bond, modeling a chemical structure graph according to the chemical structure information, the chemical structure graph including a first node corresponding to the atom and a first edge corresponding to the chemical bond, constructing an original node feature of the first node and an original edge feature of the first edge, performing a message propagation on the first edge according to the original node feature of the first node and the original edge feature of the first edge to obtain propagation state information of the first edge, and predicting properties of the target compound according to the propagation state information of the first edge.

Abstract: The present disclosure describes methods, apparatus, and storage medium for node classification and training a node classification model. The method includes obtaining a target node subset and a neighbor node subset corresponding to the target node subset from a sample node set labeled with a target node class, a neighbor node in the neighbor node subset being associated with a target node in the target node subset; extracting a feature subset of the target node subset based on the neighbor node subset by using a node classification model, the feature subset comprising a feature vector of the target node; performing class prediction for the target node subset according to the feature subset, to obtain a predicted class probability subset; and training the node classification model with a target model parameter according to the predicted class probability subset and a target node class subset of the target node subset.

Abstract: A compound property analysis method is provided. The method includes obtaining, according to a molecular structure of a compound, a feature vector of the compound, the feature vector including a node vector of each node and an edge vector of each edge, processing the feature vector by using a feature map extraction model branch to obtain a graph representation vector, and processing the graph representation vector by using a classification model branch to obtain a property of the compound. Thus, in the process of compound property analysis, the graph representation vector that can accurately represent a feature of the compound is obtained based on a graph data structure of the compound, and a classification property of the compound may be obtained based on the graph representation vector, thereby improving the accuracy of determining the classification property of the compound. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also provided.

Abstract: A compound property prediction method is provided for an electronic device. The method includes obtaining chemical structure information of a target compound, the chemical structure information including an atom and a chemical bond, modeling a chemical structure graph according to the chemical structure information, the chemical structure graph including a first node corresponding to the atom and a first edge corresponding to the chemical bond, constructing an original node feature of the first node and an original edge feature of the first edge, performing a message propagation on the first edge according to the original node feature of the first node and the original edge feature of the first edge to obtain propagation state information of the first edge, and predicting properties of the target compound according to the propagation state information of the first edge.

Abstract: The present disclosure describes methods, apparatus, and storage medium for node classification and training a node classification model. The method includes obtaining a target node subset and a neighbor node subset corresponding to the target node subset from a sample node set labeled with a target node class, a neighbor node in the neighbor node subset being associated with a target node in the target node subset; extracting a feature subset of the target node subset based on the neighbor node subset by using a node classification model, the feature subset comprising a feature vector of the target node; performing class prediction for the target node subset according to the feature subset, to obtain a predicted class probability subset; and training the node classification model with a target model parameter according to the predicted class probability subset and a target node class subset of the target node subset.