The present disclosure relates to neural network model processing methods. One example method includes obtaining an operation process of a neural network model, where the operation process is represented by at least one first-type operator and a plurality of second-type operators, and obtaining a first computation graph of the neural network model based on the operation process. In the operation process, the first-type operator includes a boundary identifier, and computational logic of the first-type operator is represented by a group of second-type operators. For any first-type operator, a range of second-type operators included in the any first-type operator is indicated by a boundary identifier in the any first-type operator.

Methods, apparatuses, and systems related to an apparatus are described. The apparatus may include (1) a fuse array configured to provide non-volatile storage of fuse data and (2) local latches configured to store the fuse data during runtime of the apparatus. The apparatus may further include an error processing circuit configured to determine error detection-correction data for the fuse data. The apparatus may subsequently broadcast data stored in the local latches to the error processing circuit to determine, using the error detection-correction data, whether the locally latched data has been corrupted. The error processing circuit may generate corrected data to replace the locally latched data based on determining corruption in the locally latched data.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

In some examples, a method of vector-raster data fusion includes receiving vector data for a geographical location, and statistically analyzing the vector data to obtain vector statistics. In some examples the method further includes rasterizing the vector statistics, and storing at least one of the vector data and the rasterized vector statistics together in a key-value store together with previously stored raster data for the geographical location. In some examples, the vector data further includes metadata, and the method further includes storing the metadata in at least one of the key-value store or a separate vector database.

The present application relates to an object prediction method and apparatus, an electronic device, and a storage medium. The method is applied to a neural network and includes: performing feature extraction processing on a to-be-predicted object to obtain feature information of the to-be-predicted object; determining multiple intermediate prediction results for the to-be-predicted object according to the feature information; performing fusion processing on the multiple intermediate prediction results to obtain fusion information; and determining multiple target prediction results for the to-be-predicted object according to the fusion information. According to embodiments of the present application, feature information of a to-be-predicted object may be extracted; multiple intermediate prediction results for the to-be-predicted object are determined according to the feature information; fusion processing is performed on the multiple intermediate prediction results to obtain fusion information; and multiple target prediction results for the to-be-predicted object are determined according to the fusion information. The method facilitates improving the accuracy of multiple target prediction results.

A system includes first and second sensors and a controller. The first sensor is of a first type and is configured to sense objects around a vehicle and to capture first data about the objects in a frame. The second sensor is of a second type and is configured to sense the objects around the vehicle and to capture second data about the objects in the frame. The controller is configured to down-sample the first and second data to generate down-sampled first and second data having a lower resolution than the first and second data. The controller is configured to identify a first set of the objects by processing the down-sampled first and second data having the lower resolution. The controller is configured to identify a second set of the objects by selectively processing the first and second data from the frame.

An object is recognized from image data as a target object and linked to a user based on an interaction by the user, information about the target object is obtained and a purchase of the target object is initiated.

A computing system accesses one or more data sources to determine maintenance optimization data associated with an asset within a set of assets. The maintenance optimization data may include one or more of: upcoming maintenance events for the asset, such as may be predicted based on analysis of historical maintenance information of the asset, a time series of predicted value of the asset over a time period around the upcoming maintenance event, such as within a few days or hours of the maintenance event, and/or a recommended window of time to initiate and/or perform upcoming maintenance events, which may be based on a combination of the expected upcoming maintenance events, and the time series of predicted value of the particular asset, for example.

A system maintains a knowledge layout to support the building of event response recommendations. Meta-graph patterns may be used to determine semantic relatedness between events and actions in response. Event-action node pairs are then constructed.

The present disclosure describes human body attribute recognition methods and apparatus, electronic devices, and a storage medium. The method includes acquiring a sample image containing a plurality of to-be-detected areas being labeled with true values of human body attributes; generating, through a recognition model, a heat map of the sample image and heat maps of the to-be-detected areas to obtain a global heat map and local heat maps; fusing the global and the local heat maps to obtain a fused image, and performing human body attribute recognition on the fused image to obtain predicted values; determining a focus area of each type of human body attribute according to the global and the local heat maps; correcting the recognition model by using the focus area, the true values, and the predicted values; and performing, based on the corrected recognition model, human body attribute recognition on a to-be-recognized image.