ar X iv : 1 20 1 . 03 75 v 2 [ cs . S I ] 5 M ay 2 01 2 Gossip on Weighted Networks

ar X iv : 1 20 1 . 03 75 v 1 [ cs . S I ] 1 J an 2 01 2 Gossip on Weighted Networks

We investigate how suitable a weighted network is for gossip spreading. The proposed model is based on the gossip spreading model introduced by Lind et.al. on unweighted networks. Weight represents “friendship. Potential spreader prefers not to spread if the victim of gossip is a “close friend”. Gossip spreading is related to the triangles and cascades of triangles. It gives more insight about ...

ar X iv : 1 10 5 . 38 75 v 1 [ co nd - m at . su pr - c on ] 19 M ay 2 01 1 Tunnel Spectroscopy of a Proximity Josephson Junction

ar X iv : 0 80 5 . 10 03 v 1 [ m at h . SP ] 7 M ay 2 00 8 GEODESICS ON WEIGHTED PROJECTIVE SPACES

We study the inverse spectral problem for weighted projective spaces using wave-trace methods. We show that in many cases one can “hear” the weights of a weighted projective space.

ar X iv : c s / 01 10 03 8 v 1 [ cs . C C ] 1 8 O ct 2 00 1 Counting Is Easy †

For any fixed k, a remarkably simple single-tape Turing machine can simulate k independent counters in real time.

ar X iv : 1 10 5 . 29 42 v 2 [ cs . D S ] 1 7 M ay 2 01 1 Invitation to Algorithmic Uses of Inclusion – Exclusion

I give an introduction to algorithmic uses of the principle of inclusion–exclusion. The presentation is intended to be be concrete and accessible, at the expense of generality and comprehensiveness. R T 1 The principle of inclusion–exclusion. There are as many odd-sized as even-sized subsets sandwiched between two different sets: For R ⊆ T ,