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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Let $S$ and $\tilde S$ be two independent and identically distributed randomvariables, which we interpret as the signal, and let $P_1$ and $P_2$ be twocommunication channels. We can choose between two measurement scenarios: eitherwe observe $S$ through $P_1$ and $P_2$, and also $\tilde S$ through $P_1$ and$P_2$; or we observe $S$ twice through $P_1$, and $\tilde{S}$ twice through$P_2$. In which of these two scenarios do we obtain the most information on thesignal $(S, \tilde S)$? While the first scenario always yields more informationwhen $P_1$ and $P_2$ are additive Gaussian channels, we give examples showingthat this property does not extend to arbitrary channels. As a consequence ofthis result, we show that the continuous-time mutual information arising in thesetting of community detection on sparse stochastic block models is notconcave, even in the limit of large system size. This stands in contrast to thecase of models with diverging average degree, and brings additional challengesto the analysis of the asymptotic behavior of this quantity.

Breakdown of a concavity property of mutual information for non-Gaussian channels