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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The foraging problem asks how a collective of particles with limitedcomputational, communication and movement capabilities can autonomouslycompress around a food source and disperse when the food is depleted orshifted, which may occur at arbitrary times. We would like the particles toiteratively self-organize, using only local interactions, to correctly gatherwhenever a food particle remains in a position long enough and search if nofood particle has existed recently. Unlike previous approaches, these searchand gather phases should be self-induced so as to be indefinitely repeatable asthe food evolves, with microscopic changes to the food triggering macroscopic,system-wide phase transitions. We present a stochastic foraging algorithm basedon a phase change in the fixed magnetization Ising model from statisticalphysics: Our algorithm is the first to leverage self-induced phase changes asan algorithmic tool. A key component of our algorithm is a careful tokenpassing mechanism ensuring a dispersion broadcast wave will always outpace acompression wave. We also present a highly structured alternative algorithmthat gathers by incrementally building a spiral tightly wrapped around the foodparticle.

Foraging in Particle Systems via Self-Induced Phase Changes