Abstract: Approximate K Nearest Neighbor (AKNN) search in high-dimensional spaces is a critical yet challenging problem. In AKNN search, distance computation is the core task that dominates the ...

Embedding-based search outperforms traditional keyword-based methods across various domains by capturing semantic similarity using dense vector representations and approximate nearest neighbor (ANN) ...

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A sophisticated cyber-espionage attack used by notorious Russian advanced persistent threat (APT) Fancy Bear at the outset of the current Russia-Ukraine war demonstrates a novel attack vector that a ...

Graph-based methods have become increasingly important in data retrieval and machine learning, particularly in nearest neighbor (NN) search. NN search helps identify data points closest to a given ...

The k-nearest neighbors (KNN) regression method, known for its nonparametric nature, is highly valued for its simplicity and its effectiveness in handling complex structured data, particularly in big ...

Python is convenient and flexible, yet notably slower than other languages for raw computational speed. The Python ecosystem has compensated with tools that make crunching numbers at scale in Python ...

ABSTRACT: Using resting-state functional magnetic resonance imaging (fMRI) technology to assist in identifying brain diseases has great potential. In the identification of brain diseases, graph-based ...