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Using scanning susceptibility microscopy, we shed new light on the dynamics of individual superconducting vortices and examine the hypotheses of the phenomenological models traditionally used to explain the macroscopic ac electromagnetic properties of superconductors. The measurements, carried out on a 2H-NbSe$_2$ single crystal at relatively high temperature $T=6.8$ K, show a linear amplitude dependence of the global ac-susceptibility for excitation amplitudes between 0.3 and 2.6 Oe. We observe that the low amplitude behavior, typically attributed to the shaking of vortices in a potential well defined by a single, relaxing, Labusch constant, corresponds actually to strongly non-uniform vortex shaking. This is particularly accentuated in the field-cooled disordered phase, which undergoes a dynamic reorganization above 0.8 Oe as evidenced by the healing of lattice defects and a more uniform oscillation of vortices. These observations are corroborated by molecular dynamics simulations when choosing the microscopic input parameters from the experiments. The theoretical simulations allow us to reconstruct the vortex trajectories providing deeper insight in the thermally induced hopping dynamics and the vortex lattice reordering.
We focus on a linear chain of $N$ first-neighbor-coupled logistic maps at their edge of chaos in the presence of a common noise. This model, characterised by the coupling strength $epsilon$ and the noise width $sigma_{max}$, was recently introduced b
Current methods for training robust networks lead to a drop in test accuracy, which has led prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning. We take a closer look at this phenomenon and first show that real
We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. To this end, we propose a fine-grained analysis of state-of-the-art methods based on key elements of this framework: gradient
Codistillation has been proposed as a mechanism to share knowledge among concurrently trained models by encouraging them to represent the same function through an auxiliary loss. This contrasts with the more commonly used fully-synchronous data-paral
Dark Matter (DM) models providing possible alternative solutions to the small- scale crisis of standard cosmology are nowadays of growing interest. We consider DM interacting with light hidden fermions via well motivated fundamental operators showing