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

Identifying defective items in larger sets is a main problem with manyapplications in real life situations. We consider the problem of localizingdefective nodes in networks through an approach based on boolean networktomography (BNT), which is grounded on inferring informations from the booleanoutcomes of end-to-end measurements paths. {\em Identifiability} conditions onthe set of paths which guarantee discovering or counting unambiguously thedefective nodes are of course very relevant. We investigate old and introducenew identifiability conditions contributing this problem both from atheoretical and applied perspective. (1) What is the precise tradeoff betweennumber of nodes and number of paths such that at most $k$ nodes can beidentified unambiguously ? The answer is known only for $k=1$ and we answer thequestion for any $k$, setting a problem implicitly left open in previous works.(2) We study upper and lower bounds on the number of unambiguously identifiablenodes, introducing new identifiability conditions which strictly imply and arestrictly implied by unambiguous identifiability; (3) We use these newconditions on one side to design algorithmic heuristics to count defectivenodes in a fine-grained way, on the other side to prove the first complexityhardness results on the problem of identifying defective nodes in networks viaBNT. (4) We introduce a random model where we study lower bounds on the numberof unambiguously identifiable defective nodes and we use this model to estimatethat number on real networks by a maximum likelihood estimate approach

Counting and localizing defective nodes by Boolean network tomography