The radial oscillations of the individual bubbles in the acoustic field are described by coupled linearized Rayleigh-Plesset equations. More specifically, we will calculate the eigenmodes and their excitabilities, eigenfrequencies, densities of states, responses, absorption and participation ratios to better understand the collective dynamics of coupled bubbles and address the question of possible localization of acoustic energy in the bubble cloud. Additionally, the conductance acquires a notable asymmetry as a function of the incident electron energy, due to the topological influence of the dislocations, while resonances appear at the coupling energy of Majorana states.Ībstract: In this paper the collective oscillations of a bubble cloud in an acoustic field are theoretically analysed with concepts and techniques of condensed matter physics. The zero-temperature magneto-conductance changes from even oscillations with period phi(0)/2 (phi(0) is the flux quantum hc/e) to odd oscillations with period phi(0), when nontrivial dislocations are present and the Majorana states are sufficiently strongly coupled. We propose a simple two-terminal conductance measurement in an interferometer formed by two edge point contacts, which reveals the nature of Majorana states through the effect of dislocations. We show that the extended lattice defects naturally present in materials, dislocations, induce spin currents on the edges while keeping the bulk time-reversal symmetry intact. We also discuss the consequences of this mixing for various scenarios where multiple scalar fields play a vital role, such as inflation and low-energy compactifications of string theory.Ībstract: We propose the experimental setup of an interferometer for the observation of neutral Majorana fermions on topological insulator-superconductor-ferromagnet junctions. We analyze the dynamical mixing between these nondecoupled degrees of freedom and deduce its nontrivial contribution to the low-energy effective theory for the light modes. The curvature of the path followed by the fields can still have a profound influence on the perturbations, as modes parallel to the trajectory mix with those normal to it if the trajectory turns sharply enough. When the kinetic energy is small compared to the potential energy, the field traverses a curve close to the vacuum manifold of the potential.
#Sto potential energy entangler full
Nontrivial imprints of the "heavy" directions on the low-energy dynamics arise when the vacuum manifold of the potential does not coincide with the span of geodesics defined by the sigma model metric of the full theory. Moreover, our approach shows that important structural properties (such as the modularity used in community detection problems) are currently based on incorrect expressions, and provides the exact quantities that should replace them.Ībstract: In this work, we study the effects of field space curvature on scalar field perturbations around an arbitrary background field trajectory evolving in time. Our method reveals that the null behavior of various correlation properties is different from what was believed previously, and is highly sensitive to the particular network considered. Remarkably, the time required to obtain the expectation value of any property analytically across the entire graph ensemble is as short as that required to compute the same property using the adjacency matrix of the single original network. Here, we propose a solution to this long-standing problem by introducing a fast method that allows one to obtain expectation values and standard deviations of any topological property analytically, for any binary, weighted, directed or undirected network. Existing approaches are either computationally demanding and beyond analytic control or analytically accessible but highly approximate. However, the generation of them is still problematic. Abstract: In order to detect patterns in real networks, randomized graph ensembles that preserve only part of the topology of an observed network are systematically used as fundamental null models.
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