3,068 research outputs found
Multi-species mean-field spin-glasses. Rigorous results
We study a multi-species spin glass system where the density of each species
is kept fixed at increasing volumes. The model reduces to the
Sherrington-Kirkpatrick one for the single species case. The existence of the
thermodynamic limit is proved for all densities values under a convexity
condition on the interaction. The thermodynamic properties of the model are
investigated and the annealed, the replica symmetric and the replica symmetry
breaking bounds are proved using Guerra's scheme. The annealed approximation is
proved to be exact under a high temperature condition. We show that the replica
symmetric solution has negative entropy at low temperatures. We study the
properties of a suitably defined replica symmetry breaking solution and we
optimise it within a ziggurat ansatz. The generalized order parameter is
described by a Parisi-like partial differential equation.Comment: 17 pages, to appear in Annales Henri Poincar\`
A Hebbian approach to complex network generation
Through a redefinition of patterns in an Hopfield-like model, we introduce
and develop an approach to model discrete systems made up of many, interacting
components with inner degrees of freedom. Our approach clarifies the intrinsic
connection between the kind of interactions among components and the emergent
topology describing the system itself; also, it allows to effectively address
the statistical mechanics on the resulting networks. Indeed, a wide class of
analytically treatable, weighted random graphs with a tunable level of
correlation can be recovered and controlled. We especially focus on the case of
imitative couplings among components endowed with similar patterns (i.e.
attributes), which, as we show, naturally and without any a-priori assumption,
gives rise to small-world effects. We also solve the thermodynamics (at a
replica symmetric level) by extending the double stochastic stability
technique: free energy, self consistency relations and fluctuation analysis for
a picture of criticality are obtained
Ferromagnetic models for cooperative behavior: Revisiting Universality in complex phenomena
Ferromagnetic models are harmonic oscillators in statistical mechanics.
Beyond their original scope in tackling phase transition and symmetry breaking
in theoretical physics, they are nowadays experiencing a renewal applicative
interest as they capture the main features of disparate complex phenomena,
whose quantitative investigation in the past were forbidden due to data
lacking. After a streamlined introduction to these models, suitably embedded on
random graphs, aim of the present paper is to show their importance in a
plethora of widespread research fields, so to highlight the unifying framework
reached by using statistical mechanics as a tool for their investigation.
Specifically we will deal with examples stemmed from sociology, chemistry,
cybernetics (electronics) and biology (immunology).Comment: Contributing to the proceedings of the Conference "Mathematical
models and methods for Planet Heart", INdAM, Rome 201
Criticality in diluted ferromagnet
We perform a detailed study of the critical behavior of the mean field
diluted Ising ferromagnet by analytical and numerical tools. We obtain
self-averaging for the magnetization and write down an expansion for the free
energy close to the critical line. The scaling of the magnetization is also
rigorously obtained and compared with extensive Monte Carlo simulations. We
explain the transition from an ergodic region to a non trivial phase by
commutativity breaking of the infinite volume limit and a suitable vanishing
field. We find full agreement among theory, simulations and previous results.Comment: 23 pages, 3 figure
Analogue neural networks on correlated random graphs
We consider a generalization of the Hopfield model, where the entries of
patterns are Gaussian and diluted. We focus on the high-storage regime and we
investigate analytically the topological properties of the emergent network, as
well as the thermodynamic properties of the model. We find that, by properly
tuning the dilution in the pattern entries, the network can recover different
topological regimes characterized by peculiar scalings of the average
coordination number with respect to the system size. The structure is also
shown to exhibit a large degree of cliquishness, even when very sparse.
Moreover, we obtain explicitly the replica symmetric free energy and the
self-consistency equations for the overlaps (order parameters of the theory),
which turn out to be classical weighted sums of 'sub-overlaps' defined on all
possible sub-graphs. Finally, a study of criticality is performed through a
small-overlap expansion of the self-consistencies and through a whole
fluctuation theory developed for their rescaled correlations: Both approaches
show that the net effect of dilution in pattern entries is to rescale the
critical noise level at which ergodicity breaks down.Comment: 34 pages, 3 figure
Equilibrium statistical mechanics on correlated random graphs
Biological and social networks have recently attracted enormous attention
between physicists. Among several, two main aspects may be stressed: A non
trivial topology of the graph describing the mutual interactions between agents
exists and/or, typically, such interactions are essentially (weighted)
imitative. Despite such aspects are widely accepted and empirically confirmed,
the schemes currently exploited in order to generate the expected topology are
based on a-priori assumptions and in most cases still implement constant
intensities for links. Here we propose a simple shift in the definition of
patterns in an Hopfield model to convert frustration into dilution: By varying
the bias of the pattern distribution, the network topology -which is generated
by the reciprocal affinities among agents - crosses various well known regimes
(fully connected, linearly diverging connectivity, extreme dilution scenario,
no network), coupled with small world properties, which, in this context, are
emergent and no longer imposed a-priori. The model is investigated at first
focusing on these topological properties of the emergent network, then its
thermodynamics is analytically solved (at a replica symmetric level) by
extending the double stochastic stability technique, and presented together
with its fluctuation theory for a picture of criticality. At least at
equilibrium, dilution simply decreases the strength of the coupling felt by the
spins, but leaves the paramagnetic/ferromagnetic flavors unchanged. The main
difference with respect to previous investigations and a naive picture is that
within our approach replicas do not appear: instead of (multi)-overlaps as
order parameters, we introduce a class of magnetizations on all the possible
sub-graphs belonging to the main one investigated: As a consequence, for these
objects a closure for a self-consistent relation is achieved.Comment: 30 pages, 4 figure
Irreducible free energy expansion and overlaps locking in mean field spin glasses
We introduce a diagrammatic formulation for a cavity field expansion around
the critical temperature. This approach allows us to obtain a theory for the
overlap's fluctuations and, in particular, the linear part of the
Ghirlanda-Guerra relationships (GG) (often called Aizenman-Contucci polynomials
(AC)) in a very simple way. We show moreover how these constraints are
"superimposed" by the symmetry of the model with respect to the restriction
required by thermodynamic stability. Within this framework it is possible to
expand the free energy in terms of these irreducible overlaps fluctuations and
in a form that simply put in evidence how the complexity of the solution is
related to the complexity of the entropy.Comment: 19 page
Retrieving Infinite Numbers of Patterns in a Spin-Glass Model of Immune Networks
The similarity between neural and immune networks has been known for decades,
but so far we did not understand the mechanism that allows the immune system,
unlike associative neural networks, to recall and execute a large number of
memorized defense strategies {\em in parallel}. The explanation turns out to
lie in the network topology. Neurons interact typically with a large number of
other neurons, whereas interactions among lymphocytes in immune networks are
very specific, and described by graphs with finite connectivity. In this paper
we use replica techniques to solve a statistical mechanical immune network
model with `coordinator branches' (T-cells) and `effector branches' (B-cells),
and show how the finite connectivity enables the system to manage an extensive
number of immune clones simultaneously, even above the percolation threshold.
The system exhibits only weak ergodicity breaking, so that both multiple
antigen defense and homeostasis can be accomplished.Comment: Editor's Choice 201
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