4,660 research outputs found
Universality for critical heavy-tailed network models: Metric structure of maximal components
We study limits of the largest connected components (viewed as metric spaces)
obtained by critical percolation on uniformly chosen graphs and configuration
models with heavy-tailed degrees. For rank-one inhomogeneous random graphs,
such results were derived by Bhamidi, van der Hofstad, Sen [Probab. Theory
Relat. Fields 2018]. We develop general principles under which the identical
scaling limits as the rank-one case can be obtained. Of independent interest,
we derive refined asymptotics for various susceptibility functions and the
maximal diameter in the barely subcritical regime.Comment: Final published version. 47 pages, 6 figure
A simple renormalization flow for FK-percolation models
We present a setup that enables to define in a concrete way a renormalization
flow for the FK-percolation models from statistical physics (that are closely
related to Ising and Potts models). In this setting that is applicable in any
dimension of space, one can interpret perturbations of the critical
(conjectural) scaling limits in terms of stationary distributions for rather
simple Markov processes on spaces of abstract discrete weighted graphs.Comment: 12 pages, to appear in the Jean-Michel Bismut 65th anniversary volum
Universality for Random Tensors
We prove two universality results for random tensors of arbitrary rank D. We
first prove that a random tensor whose entries are N^D independent, identically
distributed, complex random variables converges in distribution in the large N
limit to the same limit as the distributional limit of a Gaussian tensor model.
This generalizes the universality of random matrices to random tensors.
We then prove a second, stronger, universality result. Under the weaker
assumption that the joint probability distribution of tensor entries is
invariant, assuming that the cumulants of this invariant distribution are
uniformly bounded, we prove that in the large N limit the tensor again
converges in distribution to the distributional limit of a Gaussian tensor
model. We emphasize that the covariance of the large N Gaussian is not
universal, but depends strongly on the details of the joint distribution.Comment: Final versio
Scaling and Universality in City Space Syntax: between Zipf and Matthew
We report about universality of rank-integration distributions of open spaces
in city space syntax similar to the famous rank-size distributions of cities
(Zipf's law). We also demonstrate that the degree of choice an open space
represents for other spaces directly linked to it in a city follows a power law
statistic. Universal statistical behavior of space syntax measures uncovers the
universality of the city creation mechanism. We suggest that the observed
universality may help to establish the international definition of a city as a
specific land use pattern.Comment: 24 pages, 5 *.eps figure
Cusp Universality for Random Matrices I: Local Law and the Complex Hermitian Case
For complex Wigner-type matrices, i.e. Hermitian random matrices with
independent, not necessarily identically distributed entries above the
diagonal, we show that at any cusp singularity of the limiting eigenvalue
distribution the local eigenvalue statistics are universal and form a Pearcey
process. Since the density of states typically exhibits only square root or
cubic root cusp singularities, our work complements previous results on the
bulk and edge universality and it thus completes the resolution of the
Wigner-Dyson-Mehta universality conjecture for the last remaining universality
type in the complex Hermitian class. Our analysis holds not only for exact
cusps, but approximate cusps as well, where an extended Pearcey process
emerges. As a main technical ingredient we prove an optimal local law at the
cusp for both symmetry classes. This result is also used in the companion paper
[arXiv:1811.04055] where the cusp universality for real symmetric Wigner-type
matrices is proven.Comment: 58 pages, 2 figures. Updated introduction and reference
Unconditionally verifiable blind computation
Blind Quantum Computing (BQC) allows a client to have a server carry out a
quantum computation for them such that the client's input, output and
computation remain private. A desirable property for any BQC protocol is
verification, whereby the client can verify with high probability whether the
server has followed the instructions of the protocol, or if there has been some
deviation resulting in a corrupted output state. A verifiable BQC protocol can
be viewed as an interactive proof system leading to consequences for complexity
theory. The authors, together with Broadbent, previously proposed a universal
and unconditionally secure BQC scheme where the client only needs to be able to
prepare single qubits in separable states randomly chosen from a finite set and
send them to the server, who has the balance of the required quantum
computational resources. In this paper we extend that protocol with new
functionality allowing blind computational basis measurements, which we use to
construct a new verifiable BQC protocol based on a new class of resource
states. We rigorously prove that the probability of failing to detect an
incorrect output is exponentially small in a security parameter, while resource
overhead remains polynomial in this parameter. The new resource state allows
entangling gates to be performed between arbitrary pairs of logical qubits with
only constant overhead. This is a significant improvement on the original
scheme, which required that all computations to be performed must first be put
into a nearest neighbour form, incurring linear overhead in the number of
qubits. Such an improvement has important consequences for efficiency and
fault-tolerance thresholds.Comment: 46 pages, 10 figures. Additional protocol added which allows
arbitrary circuits to be verified with polynomial securit
Degree correlations in scale-free null models
We study the average nearest neighbor degree of vertices with degree
. In many real-world networks with power-law degree distribution
falls off in , a property ascribed to the constraint that any two vertices
are connected by at most one edge. We show that indeed decays in in
three simple random graph null models with power-law degrees: the erased
configuration model, the rank-1 inhomogeneous random graph and the hyperbolic
random graph. We consider the large-network limit when the number of nodes
tends to infinity. We find for all three null models that starts to
decay beyond and then settles on a power law , with the degree exponent.Comment: 21 pages, 4 figure
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