The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. integration wireless networks with edge computing (namely networks) brings opportunities to address this crisis. In paper, we aim investigate prediction probability propose precautionary measures against assistance networks. Due availability recorded detention time density individuals w...

A bunk bed graph consists of two isomorphic graphs, called the upper and lower bunks, and some additional edges, called posts; each post connects a vertex in the upper bunk with the corresponding isomorphic vertex in the lower bunk. We assign a probability to each edge with each edge in the upper bunk assigned the same probability as the corresponding isomorphic edge in the lower bunk. The prob...

We propose filtering the PET sinograms with a constraint curvature motion diffusion. The edge-stopping function is computed in terms of edge probability under the assumption of contamination by Poisson noise. We show that the Chi-square is the appropriate prior for finding the edge probability in the sinogram noise-free gradient. Since the sinogram noise is uncorrelated and follows a Poisson di...

Caching in multi-cell networks faces a well-known dilemma, i.e., to cache same contents among multiple edge nodes to enable transmission cooperation/diversity for higher transmission efficiency, or to cache different contents to enable content diversity for higher cache hit rate. In this work, we introduce a partition-based caching to exploit the tradeoff between transmission diversity and cont...

This paper explores the relationship between two ideas in network information theory: edge removal and strong converses. Edge removal properties state that if an edge of small capacity is removed from a network, the capacity region does not change too much. Strong converses state that, for rates outside the capacity region, the probability of error converges to 1. Various notions of edge remova...

We study the stochastic matching problem on k-uniform hypergraphs. In this problem, we are given a hypergraphH on V vertices. With each hyperedge E, we are given a probability pe of its existence i.e. an edge e exists in the hypergraph with probability pe , independent of all other edges. Each edge also has a weight associated with it. Our goal is to construct a matching with maximum expected w...

A fundamental result by Karger [10] states that for any λ-edgeconnected graph with n nodes, independently sampling each edge with probability p = Ω(logn/λ) results in a graph that has edge connectivity Ω(λp), with high probability. This paper proves the analogous result for vertex connectivity, when sampling vertices. We show that for any k-vertex-connected graph G with n nodes, if each node is...

one of the locations of renal artery atherosclerosis is at the orifice of the renal artery, therefore thestructure of this orifice was assessed in 6 normal, adult male dogs by light microscopy (lm) andtransmission electron microscopy (tem). for the lm study, processed tissues were embedded in paraffinand sectioned serially into 6-μm thickness. sections were stained with orcein. for the tem stud...

1. Let G = (V,E) be a c-edge connected graph. In other words, assume that the size of minimum cut in G is at least c. Construct a graph G′(V,E ′) by sampling each edge of G with probability p independently at random and reweighing each edge with weight 1/p. Suppose c > log n, and is such that log(n) c 2 ≤ 1. Show that as long as p ≥ log(n) c 2 , with high probability the size of every cut in G′...