English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Locality-sensitive hashing (LSH) is a fundamental technique for similaritysearch and similarity estimation in high-dimensional spaces. The basic idea isthat similar objects should produce hash collisions with probabilitysignificantly larger than objects with low similarity. We consider LSH forobjects that can be represented as point sets in either one or two dimensions.To make the point sets finite size we consider the subset of points on a grid.Directly applying LSH (e.g. min-wise hashing) to these point sets would requiretime proportional to the number of points. We seek to achieve time that is muchlower than direct approaches.  Technically, we introduce new primitives for range-efficient consistentsampling (of independent interest), and show how to turn such samples into LSHvalues. Another application of our technique is a data structure for quicklyestimating the size of the intersection or union of a set of preprocessedpolygons. Curiously, our consistent sampling method uses transformation to ageometric problem.

Range-efficient consistent sampling and locality-sensitive hashing for polygons