I-BBS recovers latent manifold dimension d and geometry from ambient distance matrices via two noise-stable integer signatures: top non-Perron multiplet multiplicity and a parameter-free shrinkage law.
Euclidean random matrices: solved and open problems
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abstract
In this paper I will describe some results that have been recently obtained in the study of random Euclidean matrices, i.e. matrices that are functions of random points in Euclidean space. In the case of {\sl translation invariant} matrices one generically finds a phase transition between a {\sl phonon} phase and a {\sl saddle} phase. If we apply these considerations to the study of the Hessian of the Hamiltonian of the particles of a fluid, we find that this phonon-saddle transition corresponds to the dynamical phase transition in glasses, that has been studied in the framework of the mode coupling approximation. The Boson peak observed in glasses at low temperature is a remanent of this transition. We finally present some recent results obtained with a new approach where one deeply uses some hidden supersymmetric properties of the problem.
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I-BBS: Coordinate-Free Inference of Latent Sub-Manifolds Using Random Distance Matrix Theory
I-BBS recovers latent manifold dimension d and geometry from ambient distance matrices via two noise-stable integer signatures: top non-Perron multiplet multiplicity and a parameter-free shrinkage law.