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Simone Franchini

Publications and source records attributed to Simone Franchini.

At least 19 recordsLinked to original sources

The Constructive Cavity Method for the Ising Perceptron

We apply the Constructive Cavity Method (CCM) to the Ising perceptron. The process starts from positive soft channels admitting an auxiliary representation $$ U(x)=\int_{\mathbb{R}} e^{tx}ρ(\mathrm{d}t),\qquad ρ\ge0, $$ with an auxiliary measure having a finite exponential quadratic moment. This rewrites the perceptron as a dense, bipartite Gaussian model with an Ising layer and a continuous auxiliary layer. The CCM blanket geometry then gives two constructive variance profiles, $Q_\ell$ on the Ising side and $Γ_\ell$ on the auxiliary side. The bipartite shell scale is $$ Q_{\ell+1}Γ_{\ell+1}-Q_\ellΓ_\ell, $$ and Gaussian core comparison removes the core contribution $ΔQ_\ellΔΓ_\ell$ under blanket refinement. The scalar cavity functional has three terms, $$ \mathcal{A}_{\mathrm{perc}}(ν)=\mathbb{E}\left[\log\langle2\cosh x\rangle_ν+α\log\langle U(s)\rangle_ν-\log\langle e^y\rangle_ν\right], $$ where $x$ is the new-spin field, $s$ is the new-constraint field, and $y$ is the cavity correction. CCM identifies the constructive cavity variables with the Aizenman--Sims--Starr random overlap structure (ROSt) variables. Parisi's recursive averaging then gives $$ \mathcal{G}_{\mathrm{perc}}^{\mathrm{CCM}}(ζ,Q,Γ)=X_0(0)+αY_0(0)-\frac{1}{2}\sum_{\ell=0}^{R}ζ_\ell\left(Q_{\ell+1}Γ_{\ell+1}-Q_\ellΓ_\ell\right). $$ The replica-symmetric specialization gives Gardner's formula for the generalized Ising perceptron. Specializing the constraint terminal to the hard channel gives the Krauth--Mézard functional for the binary perceptron.

cond-mat.stat-mech↗

Constructive Cavity Method for Ising Restricted and Deep Boltzmann Machines with Gaussian Couplings

We apply the Constructive Cavity Method (CCM) to the dense, Ising type, restricted and deep Boltzmann machine with Gaussian couplings between adjacent layers. After identifying the core and interface contributions, CCM identifies the blanket decomposition of the cavity variables with the Aizenman--Sims--Starr random overlap structure. We apply the Random Energy Model (REM) universality ansatz to represent the cavity free energies by those of a generalized random energy model (GREM) and evaluate them recursively through Poisson point processes (PPPs). In the layer-wise gauge each layer is cut with its own cumulative fraction $|U_\ell\cap L_p|=λ_p q_\ell^{(p)}N+o(N)$, with $\mathbf 0=\mathbf q_0\le\mathbf q_1\le\cdots\le\mathbf q_r=\mathbf 1$, where $\mathbf q_\ell=(q_\ell^{(1)},\ldots,q_\ell^{(K)})$. Here $U_\ell$ is the cumulative blanket union up to blanket $\ell$. The index $p$ labels architectural layers, whereas $\ell$ labels the blankets. The dependence on the multilayer geometry is carried by the two matrices $M=2M_0Λ$ and $M_1=ΛM_0Λ$. The constructive cavity field in layer $p$ has blanket variances $Q_\ell^{(p)}-Q_{\ell-1}^{(p)}=(M(\mathbf q_\ell-\mathbf q_{\ell-1}))_p$, while the correction field has blanket variances $Q_\ell-Q_{\ell-1}=\mathbf q_\ell^\top M_1\mathbf q_\ell-\mathbf q_{\ell-1}^\top M_1\mathbf q_{\ell-1}$. The nested REM--PPP average gives the finite-level constructive cavity functional $\mathcal{P}_{\mathrm{RBM/DBM}}^{\mathrm{CCM}}(ζ,\mathbf q)=\log2+\sum_{p=1}^Kλ_pX_0^{(p)}-\frac12\sum_{\ell=0}^{r-1}ζ_\ell(\mathbf q_{\ell+1}^\top M_1\mathbf q_{\ell+1}-\mathbf q_\ell^\top M_1\mathbf q_\ell)$. The balanced scalar gauge is recovered by imposing $\mathbf q_\ell=q_\ell\mathbf 1$. Selecting the physical value in the non-convex multilayer regime is a separate problem.

cond-mat.stat-mech↗

Simplified Parisi Ansatz III: Constructive Cavity Method and Ultrametricity

We present a physical explaination of the Constructive Cavity Method (CCM) for the Sherrington--Kirkpatrick model, which identifies the decomposition of the cavity variables into layers with the random overlap structure of Aizenman--Sims--Starr. We describe the low-temperature excitations through a dilute gas of defects relative to the ground state. The sharing of these defects among the descendants and the renewal of the fluctuations along new branches produce the GREM structure and the ultrametricity of the overlaps. This mechanism is developed first with a finite number of defects and then extended to exponential populations of states. At fixed kernel, the Gaussian covariances of the fresh fields connect the complete cavity variables, including their common components, to the same hierarchy that determines the Gibbs masses. We also correct some ambiguities of earlier formulations.

cond-mat.stat-mech↗

REM universality and Poisson-Dirichlet Gibbs weights for linear random energy

We study the Hamiltonian $H_n(h,σ)=\sum_{i=1}^n h_i(σ_i-m), $ where $(h_i)$ are i.i.d.\ real random variables and $(σ_i)$ are i.i.d.\ Ising spins. We consider the energy levels obtained after an independent thinning that retains an exponential number of configurations ($e^{O(n)}$). We prove that, after an $(h_i)$-dependent centering, the resulting point process converges in distribution to a Poisson point process with exponential intensity. Thus, the energy levels asymptotically has the one of the Random Energy Model (REM). Our results extend previous ones, where REM universality for this model was established only either for energy fluctuations of order $e^{-O(n)}$ or for $e^{o(\sqrt n)}$ randomly selected configurations. We also identify the limiting Gibbs weights, which converge to a Poisson--Dirichlet law, and the quenched free energy, which exhibits a freezing transition at $β=\tildeλ$. The proofs are presented here in compressed form; full details are given in the companion preprint.

math.PR↗

A simplified Parisi Ansatz II: REM universality

In a previous work [A simplified Parisi Ansatz, Franchini, S., Commun. Theor. Phys., 73, 055601 (2021)] we introduced a simple method to compute the Random Overlap Structure of Aizenmann, Simm and Stars and the full RSB Parisi formula for the Sherrington-Kirkpatrick Model without using replica theory. The method consists in partitioning the system into smaller sub-systems that we call layers, and iterate the Bayes rule. A central ansatz in our derivation was that these layers could be approximated by Random Energy Models of the Derrida type. In this paper we analyze the properties of the interface in detail, and show the equivalence with the Random Energy Model at any temperature.

cond-mat.stat-mech↗

Universal scaling limits for spin networks via martingale methods

We use simple martingale methods to construct a large deviation theory of spin systems with pairwise interactions. As an application, we show that the fully connected case obeys a universal scaling limit that is just a product of magnetisation eigenstates.

cond-mat.stat-mech↗

REM universality for linear random energy

We consider a sequence of random Hamiltonians $H_n(h,σ)=\sum^n_{i=1}h_i(σ_i-m)$, and study the asymptotic ($n\to \infty$) distribution of the energy levels $(H_n(h,σ))_{σ\in \{-1,1\}^n}$, where $h_1,h_2,\cdots$ are i.i.d. random variables. We show that, when $e^{O(n)}$ configurations are sampled at random, the corresponding collection of energy levels converges in distribution to a Poisson point process with exponential intensity measure. This establishes the Random Energy Model (REM) universality for the present model. Our results strengthen earlier works on local REM universality by characterizing the distribution of $O(1)-$order fluctuations of $H_n$. In addition, we improve upon the REM universality by dilution studied by Ben Arous, Gayrard, Kuptsov by allowing an exponentially large number $e^{O(n)}$ of sampled configurations, instead of $e^{o(\sqrt{n})}$. Finally, we derive the asymptotic distribution of the Gibbs weight.

math.PR↗

Lattice Field Theory for a network of real neurons

In a recent paper [Bardella et al., Entropy 26 (6), 495 (2024)] we introduced a simplified Lattice Field Theory (LFT) framework that allows experimental recordings from major Brain-Computer Interfaces (BCIs) to be interpreted in a simple and physically grounded way. From a neuroscience point of view, our method modifies the Maximum Entropy model for neural networks so that also the time evolution of the system is taken into account and it can be interpreted as another version of the Free Energy principle (FEP). This framework is naturally tailored to interpret recordings from chronic multi-site BCIs, especially spike rasters from measurements of single neuron activity.

cond-mat.stat-mech↗

Constructive Cavity Method

We show that the functional appearing in the celebrated Parisi formula for the free energy of the Sherrington-Kirkpatrick model can be found from the incremental free energy obtained by Cavity Method if one assumes that the state is a product of independent Random Energy models.

cond-mat.stat-mech↗

Large deviations for Generalized Polya Urns with non-binary increments

In this paper we show how to extend the Sample-Path Large Deviation Principle for the urn model of Hill, Lane and Sudderth to the case in which the increment of the urn is not a binary variable. In particular, we sketch how to modify the Theorem 1 given in [Stochastic Processes and their Applications 127 (2017) 3372-3411] to include also urn processes with increments taking more than two values.

math.PR↗

The Urn of Hill, Lane and Sudderth

We review some facts, properties and applications of the urn of Hill, Lane and Sudderth, a paradigmatic model of stochastic process with memory where the urn evolution is as follows: consider an urn of given capacity, at each step a new ball, black or white, is added to the urn with probability that is function (urn function) of the fraction of black balls. The process runs until capacity is reached.

math.PR↗

P-adic numbers and kernels

We discuss the relation between p-adic numbers and kernels in view of a recent large deviation theory for mean-field spin glasses. As an application we show several fundamental properties of numerical bases in kernel language. In particular, we show that the Derrida's Generalized Random Energy Model can be interpreted as a (random) numerical base. We also show an application to the Primon gas and the Riemann Zeta Function by constructing a kernel representation of the Primon gas based on a finite p-base, thereby establishing a concrete link between number theory and kernel theory.

cond-mat.dis-nn↗

Blanket representation for spin networks with n-body interactions

This paper extends the Blanket representation of [Universal scaling limits for spin networks via martingale methods, Franchini, S., Proc. R. Soc. A, 481 (2025)] from systems with two-body interactions to multi-spin (or n-body) interactions. This generalization allows for the exploration of broader physical phenomena where higher order interactions are significant.

cond-mat.stat-mech↗

On the Pure States of the Replica Symmetry Breaking ansatz

We discuss the concept of Pure State of the Replica Symmetry Breaking ansatz in finite and infinite spin systems without averaging on the disorder, nor using replicas. Consider a system of n spins $σ\inΩ^{n}$ with the usual set $Ω=\left\{ -1,1\right\}$ of inner states and let $G:\,Ω^{n}\rightarrow\left[0,1\right]$ a Gibbs measure on it of Hamiltonian $\mathcal{H}$ (also non random). We interpret the pure states of a model $\left(Ω^{n},μ\right)$ as disjoint subsets $Ω^{n}$ such that the conditional measures behaves like product measures as in usual mean field approximations. Starting from such definition we try to reinterpret the RSB scheme and define an approximated probability measure. We then apply our results to the Sherrington-Kirkpatrick model to obtain the Parisi formula.

cond-mat.stat-mech↗

Elephant Random Walk with multiple extractions

Consider a generalized Elephant Random Walk in which the step is chosen by selecting $k$ previous steps with $k$ odd and then going in the majority direction with a probability $p$ and in the opposite direction otherwise. In the $k=1$ case the model is the original one and could be resolved exactly by analogy with Friedman's urn. However the analogy cannot be extended to the $k>2$ case already. In this paper we show how to treat the model for each $k$ by analogy with the more general urn model of Hill, Lane and Sudderth. Interestingly for $k>2$ we found a critical dependence from the initial conditions beyond a certain values of the memory parameter $p$, and regions of convergence with entropy that is sub-linear in the number of steps.

math.PR↗

Large deviations for Generalized Polya Urns with arbitrary urn function

We consider a generalized two-color Polya urn (black and withe balls) first introduced by Hill, Lane, Sudderth where the urn composition evolves as follows: let $π:\left[0,1\right]\rightarrow\left[0,1\right]$, and denote by $x_{n}$ the fraction of black balls after step $n$, then at step $n+1$ a black ball is added with probability $π\left(x_{n}\right)$ and a white ball is added with probability $1-π\left(x_{n}\right)$. Originally introduced to mimic attachment under imperfect information, this model has found applications in many fields, ranging from Market Share modeling to polymer physics and biology. In this work we discuss large deviations for a wide class of continuous urn functions $π$. In particular, we prove that this process satisfies a Sample-Path Large Deviations principle, also providing a variational representation for the rate function. Then, we derive a variational representation for the limit $ϕ\left(s\right)=\lim_{n\rightarrow\infty}{\textstyle \frac{1}{n}}\log\mathbb{P}\left(\left\{ nx_{n}=\left\lfloor sn\right\rfloor \right\} \right),\, s\in\left[0,1\right]$, where $nx_{n}$ is the number of black balls at time $n$, and use it to give some insight on the shape of $ϕ\left(s\right)$. Under suitable assumptions on $π$ we are able to identify the optimal trajectory. We also find a non-linear Cauchy problem for the Cumulant Generating Function and provide an explicit analysis for some selected examples. In particular we discuss the linear case, which embeds the Bagchi-Pal Model, giving the exact implicit expression for $ϕ$ in terms of the Cumulant Generating Function.

math.PR↗

Energy of the Interacting Self-Avoiding Walk at the $θ-$point

We perform a numerical study of a new microcanonical polymer model on the three dimensional cubic lattice, consisting of ideal chains whose range and number of nearest-neighbor contacts are fixed to given values. Our simulations suggest an interesting exact relation concerning the internal energy per monomer of the Interacting Self-Avoiding Walk at the $θ-$point.

cond-mat.stat-mech↗

Large Deviations Theory of Increasing Returns

An influential theory of increasing returns has been proposed by the economist W. B. Arthur in the '80s to explain the lock-in phenomenon between two competing commercial products. In the most simplified situation there are two competing products that gain customers according to a majority mechanism: each new customer arrives and asks which product they bought to a certain odd number of previous customers, and then buy the most shared product within this sample. It is known that one of these two companies reaches monopoly almost surely in the limit of infinite customers. Here we consider a generalization [G. Dosi, Y. Ermoliev, Y. Kaniovsky, J. Math. Econom. 23, 1-19 (1994)] where the new customer follows the indication of the sample with some probability, and buy the other product otherwise. Other than economy, this model can be reduced to the urn of Hill, Lane and Sudderth, and includes several models of physical interest as special cases, like the Elephant Random Walk, the Friedman's urn and other generalized urn models. We provide a large deviation analysis of this model at the sample-path level, and give a formula that allows to find the most likely trajectories followed by the market share variable. Interestingly, in the parameter range where the lock-in phase is expected, we observe a whole region of convergence where the entropy cost is sub-linear. We also find a non-linear differential equation for the cumulant generating function of the market share variable, that can be studied with a suitable perturbations theory.

math.PR↗