13 results about "Pairwise similarity" patented technology

Similarity sensitive diversity is utilized to measure variation in a distribution of item listings along one or more categories. A similarity between category vectors of each category pair in a set of categories is determined and utilized to generate a pairwise similarity matrix. The pairwise similarity matrix may be pruned to remove category pairs below a threshold. Utilizing the pairwise similarity matrix, similarity sensitive diversity between one or more items of a plurality of items may be determined. In various aspects, the similarity sensitive diversity may be utilized to: generate a list of relevant items in an appropriate distribution, suggest refinements of a search query; generate navigation modules; categorize or recategorize the plurality of items; or generate autosuggestions.

The application discloses a multi-head attention mechanism-based super network representation learning method applied to breeding, relates to the technical field of crop breeding, and comprises the following steps: constructing a crop heterogeneous super network, and constructing an initial embedding matrix corresponding to all nodes; obtaining a normalized node representation vector corresponding to each tuple and a similarity score of each tuple; calculating the pairwise similarity between each two nodes; constructing a joint loss function, and optimizing the parameters of a target model; generating a final representation vector of a node in the crop heterogeneous super network by using the target model after parameter optimization; and predicting a crop variety according to the final representation vector. The application avoids complex graph conversion and explicit high-order combination calculation, improves the calculation efficiency and scalability, can more comprehensively and accurately encode semantic and structural information of phenotypic traits, and thus provides reliable and high-quality data basis for crop variety prediction based on the final representation vector.

This application discloses a method, apparatus, device, and medium for screening data features, relating to the field of data mining and analysis. The method includes: sequentially determining multiple information indicators for each candidate data feature based on a preset set of test threshold parameters; wherein the set of test threshold parameters is configured based on out-of-sample window data; screening the multiple candidate data features to obtain multiple screened data features based on the multiple information indicators and the set of test threshold parameters, combined with reference data features corresponding to each candidate data feature; wherein the reference data features are constructed based on the corresponding candidate data features; grouping the multiple screened data features according to the pairwise similarity between them to obtain multiple similar feature groups, and selecting a representative data feature for each similar feature group based on the multiple information indicators of the screened data features. By implementing this application, the accuracy of data feature screening can be improved.

A method is provided for data processing performed by a processing system. The method comprises determining a set of first tokens for first data and a set of second token for second data, each token comprising information associated with a segment of the respective data, determining pair-wise similarities between the set of first tokens and the set of second tokens, each pair comprising a first token in the set of first tokens and a second token in the set of second tokens, determining, for each first token in the set of first tokens, a maximum similarity based on the determined pair-wise similarities between the respective first token and the second tokens in the set of second tokens, and determining a first similarity between the first data and the second data by aggregating the maximum similarities corresponding to the first tokens in the set of first set of tokens.

This invention discloses a method, system, and rail vehicle for detecting axle box bearing faults, relating to the field of rail transport safety technology. It extracts frequency domain characteristic waveforms from the axle box bearing's state signals, performs pairwise similarity comparisons of the frequency domain characteristic waveforms of different components at the same axle position on the same bogie, and compares the similarity of the frequency domain characteristic waveforms of the same components at the same axle position on different bogies. By combining the comparison results of different components within the same axle position and the comparison results of the same components between different bogies, fault determination is made. This enables the detection of lateral anomalies in multiple components under the same operating conditions. Compared to traditional methods that use single-component, fixed-threshold determination, this method not only adapts to different operating conditions but also eliminates the need to pre-establish health baselines for different components. It effectively improves the accuracy of axle box bearing fault detection in rail vehicles while simplifying deployment and increasing detection sensitivity.

The present application relates to apparatuses, systems, and methods for grouping data records based on entities referenced by the data records. The disclosed grouping mechanism can include determining pairwise similarities among a large number of data records, and clustering subsets of data records based on their pairwise similarities.

This invention discloses a method for retrieving similar protein structures based on hash learning: First, a protein structure dataset is acquired, and the pairwise similarity information is calculated. For the query sample, positive and negative samples are sampled. The protein structures are modeled as a graph, and node and edge features are extracted, the feature representations of nodes and edges are updated, quantization loss and similarity loss are defined, and the final loss function is obtained. The model is then trained. Based on the trained model, each collected protein structure is represented as a binary vector, resulting in a binary vector database. During retrieval, the trained model represents the new protein structure to be retrieved as a binary vector. Similar structures are retrieved by directly calculating the Hamming distance between binary vectors or by constructing an inverted index. Alternatively, the top-ranked protein structures returned based on binary vector retrieval can be reordered using other real-valued vectors or more complex algorithms. This invention reduces storage overhead and improves retrieval speed.

In the disclosed systems and methods for categorizing medical data, a computer system obtains, in electronic form, a plurality of medical records. Each medical record includes corresponding medical data from a respective medical evaluation and corresponding metadata comprising a plurality of attributes about the respective medical evaluation. Each respective attribute comprises a corresponding string of text. The computer system determines, for each respective pair of medical records consisting of a first medical record and a second medical record, a corresponding pairwise similarity between, for each respective attribute in a set of attributes, the corresponding string of text for the first medical record and the corresponding string of text for the second medical record. The computer system identifies a first subset of the plurality of medical records. Each respective medical record in the first subset is connected to each other through pairwise similarities that each satisfies a similarity threshold.

Systems and methods for LLM assessment are disclosed herein. Embodiments may provide a quality assessment of an LLM that is based on the consistency of that LLM. Utilizing response sampling, pairwise similarity, and an uncertainty score, embodiments may measure response consistency, which can be correlated with output quality for an LLM.

The present application relates to a method for analyzing the similarity of at least two samples of a plurality of samples comprising genomic DNA. The method comprises the following steps: a) providing a plurality of samples comprising genomic DNA; b) performing a deterministic restriction site whole genome amplification (DRS-WGA) of the genomic DNA separately for each sample; c) preparing a massively parallel sequencing library from each product of DRS-WGA using a no-fragmentation, sequencing adapter / WGA fusion primer PCR reaction; d) performing low-pass whole genome sequencing of the massively parallel sequencing library at an average coverage depth of less than 1x; e) aligning the reads of each sample obtained in step d) to a reference genome; f) extracting the allele content at a plurality of polymorphic loci for each sample; g) calculating a pairwise similarity score locus for at least two samples based on the measured allele content at the plurality of loci; h) determining the similarity of at least two samples based on the similarity score, the method being used for non-invasive prenatal testing or diagnosis.

There are provided systems and methods for pairwise graph querying, merging, and computing for account linking. A service provider may provide an account graph system to identify pairwise similarities between different accounts based on shared data that may be identified through one or more linking characteristics. When providing pairwise graph similarities, a service provider may receive a query identifying two or more accounts and / or an account with a parameter for graph exploration and querying.The service provider may utilize connection, link, or relationship graphs, queried and generated using a graph database, to determine pairwise similarities between the designated seed account and one or more selected accounts. The graph may include vertices for different queried data points and edges connecting such queries, where directionality of the edges or other vectors may be used to identify links or hops between accounts for data querying and exploration.

The invention provides a video feature generation and recognition method and device and electronic equipment, and the method comprises the steps: carrying out the sampling processing of video data, obtaining a plurality of video frame nodes, carrying out the feature extraction of the video frame nodes, and obtaining a frame feature vector; calculating the similarity between every two video frame nodes based on the frame feature vectors, and constructing a feature map according to the similarity; dividing the feature map through a connected component detection algorithm to obtain a structure cluster, and dividing the feature map through a semantic clustering algorithm to obtain a semantic cluster; and determining a plurality of feature subgroups according to the intersection of the structure cluster and the semantic cluster, performing feature aggregation processing on the frame feature vector corresponding to each video frame node in each feature subgroup to obtain a video feature matrix, and inputting the video feature matrix into a video recognition model to obtain a recognition result. The problem that in an existing video frame feature generation technology, due to the fact that the inter-frame structure relation is ignored, feature aggregation is excessively smooth, and global structure sensing capacity is lacked, the model recognition effect is poor is solved.

This invention provides a method and system for predicting organoid drug synergy, relating to the field of drug combination prediction technology. The method includes: extracting drug interaction features; capturing the effects of drug combinations on organoids through information transmission between drug target networks and organoid target networks; and predicting organoid drug synergy by fusing drug interaction and target network features based on dose-efficacy analysis. This invention utilizes the similarity of drug molecule functional groups and pseudo-attention mechanisms to capture potential interaction information between drugs; obtaining drug-organoid effect features based on pairwise similarity and interactive attention fusion; and introducing single-drug dose-efficacy as key information into a conditional variational autoencoder-generative adversarial network to capture the impact of different drug doses on drug synergy effects, thereby improving the accuracy of drug synergy prediction.