2 results about "Mathematical Operators" patented technology

This invention provides a method and apparatus for quantifying continuous sentiment intensity based on multi-dimensional feature decoupling. The method identifies the attribution relationship between semantic carriers and subjects in the text to be analyzed to obtain a first feature basis vector; extracts absolute sentiment values ​​or feature vector information of words to obtain a second feature basis vector; and identifies syntactic structure level, organizational rules, or context topology layer to obtain a third feature basis vector. A high-dimensional decoupled feature space with a total dimension of is constructed, and the above three are mapped as a subset of baseline dimensions into it. This space supports dynamic insertion and modular expansion of any external variables across scenarios and topics as incremental feature dimensions, and adaptively adjusts the input matrix of the combination operator. The aggregation engine uses mathematical operators to perform spatial mapping and dimensionality reduction aggregation on the multi-dimensional feature tensor to obtain the absolute value of continuous sentiment intensity corresponding to each subject. This invention can capture structural changes and abnormal moments in sentiment intensity, demonstrating the evolution of public sentiment and opinions.

This invention relates to fused processing of continuous mathematical operators. Systems and methods for fused processing of continuous mathematical operators are disclosed. Fused processing of continuous mathematical operations (e.g., pointwise nonlinear functions) without storing intermediate results to memory improves performance when memory bus bandwidth is limited. In one embodiment, continuous mathematical operations including at least two of convolution, upsampling, pointwise nonlinear function, and downsampling are performed to process input data and generate aliasing-free output data. In one embodiment, input data is spatially blocked for parallel processing such that intermediate results generated during processing of each block of input data can be stored in shared memory within the processor. Storing intermediate data in shared memory improves performance compared to storing and loading intermediate data to and from external memory.