Related papers: When Independent Gaussian Models Break Down: Chara…
Matrix Models are the most effective way to describe strongly interacting systems with many degrees of freedom. They have proven successful in describing very different settings, from nuclei spectra to conduction in mesoscopic systems, from…
A numerical algorithm is used to solve the bare and the effective potential for the scalar $\phi^4$ model in the local potential approximation. An approximate dynamical Maxwell-cut is found which reveals itself in the degeneracy of the…
We review the gauge symmetry breaking mechanism due to orbifold projections in orbifold model building. We explicitly show the existence of a scale of breaking if such a symmetry breaking is due to freely-acting orbifold operators only,…
We present a model, in which some nodes (called interconnecting nodes) in two networks merge and play the roles in both the networks. The model analytic and simulation discussions show a monotonically increasing dependence of…
We investigate dynamical symmetry breaking in a class of chiral gauge theories containing the Georgi-Glashow model. These theories feature a gauge sector and two fermion species that transform in the two-index antisymmetric and…
Modelling of rough frictional interfaces is often based on asperity models, in which the individual behaviour of individual microjunctions is assumed. In the absence of local measurements at the microjunction scale, quantitative comparison…
Despite major methodological developments, Bayesian inference for Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a method to infer the marginal and…
The formation of scalar condensates and dynamical symmetry breaking in the U(n) four-fermion models (for n=2,3) with two coupling constants has been studied by the functional integration method. The bosonization procedures of the models…
An old problem in multivariate statistics is that linear Gaussian models are often unidentifiable, i.e. some parameters cannot be uniquely estimated. In factor (component) analysis, an orthogonal rotation of the factors is unidentifiable,…
The Schwinger model with two massive fermions is a nontrivial theory for which no analytical solution is known. The strong coupling limit of the theory allows for different semiclassical approximations to extract properties of its low-lying…
The aim of this article is to analyze data from multiple repairable systems under the presence of dependent competing risks. In order to model this dependence structure, we adopted the well-known shared frailty model. This model provides a…
We show, via numerical simulations, that the fidelity decay behavior of quasi-integrable systems is strongly dependent on the location of the initial coherent state with respect to the underlying classical phase space. In parallel to…
We investigate a quantum thermometry scheme based collision model with Gaussian systems. A key open question of these schemes concerns the scaling of the Quantum Fisher Information (QFI) with the number of ancillae. In qubit-based…
We present a model of supersymmetry breaking which produces gaugino masses and negligible scalar masses at a high scale. The model is inspired by ``deconstructing'' or ``latticizing'' models in extra dimensions where supersymmetry breaking…
To model high dimensional data, Gaussian methods are widely used since they remain tractable and yield parsimonious models by imposing strong assumptions on the data. Vine copulas are more flexible by combining arbitrary marginal…
The transfer-matrix of the U(1) lattice model is considered in the Fourier basis and in the weak coupling limit. The issues of Gauss law constraint and gauge invariant states are addressed in the Fourier basis. In particular, it is shown…
Consistent interactions that can be added to a free, Abelian gauge theory comprising a BF model and a finite set of massless real scalar fields are constructed from the deformation of the solution to the master equation based on specific…
Gaussian graphical models are widely used to infer dependence structures. Bayesian methods are appealing to quantify uncertainty associated with structural learning, i.e., the plausibility of conditional independence statements given the…
The article presents a systematic study of the problem of conditioning a Gaussian random variable $\xi$ on nonlinear observations of the form $F \circ \phi(\xi)$ where $\phi: \mathcal{X} \to \mathbb{R}^N$ is a bounded linear operator and…
Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of real-world testing is prohibitive, we propose a…