Renormalization group theory for percolation in time-varying networks

Motivated by multi-hop communication in unreliable wireless networks, we present a percolation theory for time-varying networks. We develop a renormalization group theory for a prototypical network on a regular grid, where individual links switch stochastically between active and inactive states. The question whether a given source node can communicate with a destination node along paths of active links is equivalent to a percolation problem. Our theory maps the temporal existence of multi-hop paths on an effective two-state Markov process. We show analytically how this Markov process converges towards a memory-less Bernoulli process as the hop distance between source and destination node increases. Our work extends classical percolation theory to the dynamic case and elucidates temporal correlations of message losses. Quantification of temporal correlations has implications for the design of wireless communication and control protocols, e.g. in cyber-physical systems such as self-organized swarms of drones or smart traffic networks.Comment: 8 pages, 3 figure

Results of 31 patients with EGFR mutation(s) and response to tyrosine kinase inhibitor.

<p>Results of 31 patients with EGFR mutation(s) and response to tyrosine kinase inhibitor.</p

Characteristics of 3 cases with discrepancy in PT & LN in major mutations.

<p>Characteristics of 3 cases with discrepancy in PT & LN in major mutations.</p

Baseline characteristics of 74 patients.

<p>Baseline characteristics of 74 patients.</p

Demographics and baseline characteristics for opioid groups by quartiles.

<p>Demographics and baseline characteristics for opioid groups by quartiles.</p

Odds ratios of recurrence and death for 1 unit opioid usage increase over all cancer stage.

<p>Odds ratios of recurrence and death for 1 unit opioid usage increase over all cancer stage.</p

Parameters estimated in the multivariate logistic regression model.

<p>Parameters estimated in the multivariate logistic regression model.</p