6 results about "Independent vector" patented technology

Methods and systems for image alignment are provided. In one embodiment, a method includes individually aligning candidate alignment target images in an image generated for a sample to corresponding set images of set alignment targets selected to have mutually linearly independent vectors between a position of the set alignment targets and a reference position. The method also includes, for any of the candidate images successfully aligned to their corresponding set images, individually determining coordinates of the reference position from coordinates of the aligned candidate images and their corresponding mutually linearly independent vectors. In addition, the method includes determining final coordinates of the reference position in the image generated for the sample from the individually determined coordinates of the reference position.

The present invention relates to a linear network coding method based on BLS. The source node divides the file to be signed into several file blocks and represents them as a prime domain #imgabs0# k dimensional vectors, which are then augmented to become a set of linearly independent vectors; a subspace generated based on this set of vectors is signed, and a label for the subspace and a signature for each basis vector are generated. The signatures are loaded into a data packet and sent to a downstream node; after receiving the data packet, the intermediate router or the destination node verifies the signature of the data packet from the same source node; the intermediate router linearly combines all verified data packets to form a new data packet and sends it to the downstream node; if the destination node receives a predetermined number of verified data packets, it can parse the original data sent by the source node. The present invention can not only resist pollution attacks in network coding and ensure data integrity and authenticity, but also reduce user computing complexity and ensure secure and efficient data transmission.

The application discloses a kind of multi-node base station traffic prediction methods based on self-organizing graph neural network, belong to space-time traffic prediction technical field, including base station node data processing and influence evaluation: node set subgraph is formed, and the size of influence of adjacent matrix and evaluation node is generated;Base station node reorganization: through mapping matrix, linear combination is carried out to time sequence node, generates linearly independent vector base, and according to influence guide mapping matrix convergence direction;Using part attention aggregation spatial feature: node set is sequentially carried out static graph convolution and dynamic graph convolution and captures spatial connection;In convolution, long short-term memory network is inserted: capture time connection and carry out time series prediction;Finally, standardization is carried out and is generated prediction result by multilayer perception machine.The application uses the above-mentioned one kind of multi-node base station traffic prediction methods based on self-organizing graph neural network, proposes the spatial feature extraction mechanism of multi-node base station traffic, provides effective prediction method for the change trend of base station traffic.

The invention relates to the technical field of artificial intelligence, in particular to a molecular representation distribution external generalization learning method based on annealing noise enhancement and dual vector quantization and application thereof. Extracting node characterization and invariance scores of the molecular graph by using a double-flow encoder to realize preliminary deentanglement of characterization; then, annealing noise enhancement mechanism mixed features are introduced, context dependence is reserved in the initial stage of training, two independent vector quantization experts are utilized to establish exclusive discrete potential spaces for invariant representation and false representation respectively, and the problem of information entanglement of a single codebook is solved; and meanwhile, node-level statistical independence is forcibly realized by utilizing an independence enhancement module, and joint optimization is carried out in combination with an invariant learning strategy. The objective of the invention is to solve the problem of how to realize thorough de-entanglement of invariant representation and false representation of non-Euclidean molecular graph data on a fine-grained node level.

Methods and systems for image alignment are provided. One method includes separately aligning candidate alignment target images in images generated for a specimen to corresponding setup images for setup alignment targets selected to have mutually linearly independent vectors between locations of the setup alignment targets and a reference location. The method also includes, for any of the candidate images successfully aligned to its corresponding setup image, separately determining coordinates of the reference location from coordinates of the aligned candidate images and their corresponding mutually linearly independent vectors. In addition, the method includes determining final coordinates of the reference location in the images generated for the specimen from the separately determined coordinates of the reference location.

Approaches presented herein provide systems and methods for determining duplicate objects within an interaction environment. Connectivity information for an object may be used to map a set of three linearly independent vectors corresponding to a transform applied to the object. These three linearly independent vectors may be used to form canonical forms of first and second objects to determine whether the first object and the second object are duplicates or near-duplicates. Copies of duplicate or near-duplicate objects may then be deleted from the interaction environment and represented by a common object to which one or more additional transforms are applied.