3,800 research outputs found
The twilight zone in the parametric evolution of eigenstates: beyond perturbation theory and semiclassics
Considering a quantized chaotic system, we analyze the evolution of its
eigenstates as a result of varying a control parameter. As the induced
perturbation becomes larger, there is a crossover from a perturbative to a
non-perturbative regime, which is reflected in the structural changes of the
local density of states. For the first time the {\em full} scenario is explored
for a physical system: an Aharonov-Bohm cylindrical billiard. As we vary the
magnetic flux, we discover an intermediate twilight regime where perturbative
and semiclassical features co-exist. This is in contrast with the {\em simple}
crossover from a Lorentzian to a semicircle line-shape which is found in
random-matrix models.Comment: 4 pages, 4 figures, improved versio
Contractions of low-dimensional nilpotent Jordan algebras
In this paper we classify the laws of three-dimensional and four-dimensional
nilpotent Jordan algebras over the field of complex numbers. We describe the
irreducible components of their algebraic varieties and extend contractions and
deformations among them. In particular, we prove that J2 and J3 are irreducible
and that J4 is the union of the Zariski closures of two rigid Jordan algebras.Comment: 12 pages, 3 figure
Semi-Supervised Deep Learning for Fully Convolutional Networks
Deep learning usually requires large amounts of labeled training data, but
annotating data is costly and tedious. The framework of semi-supervised
learning provides the means to use both labeled data and arbitrary amounts of
unlabeled data for training. Recently, semi-supervised deep learning has been
intensively studied for standard CNN architectures. However, Fully
Convolutional Networks (FCNs) set the state-of-the-art for many image
segmentation tasks. To the best of our knowledge, there is no existing
semi-supervised learning method for such FCNs yet. We lift the concept of
auxiliary manifold embedding for semi-supervised learning to FCNs with the help
of Random Feature Embedding. In our experiments on the challenging task of MS
Lesion Segmentation, we leverage the proposed framework for the purpose of
domain adaptation and report substantial improvements over the baseline model.Comment: 9 pages, 6 figure
Assessment of a Universal Reconfiguration-less Control Approach in Open-Phase Fault Operation for Multiphase Drives
Multiphase drives have been important in particular industry applications where reliability is
a desired goal. The main reason for this is their inherent fault tolerance. Di erent nonlinear controllers
that do not include modulation stages, like direct torque control (DTC) or model-based predictive
control (MPC), have been used in recent times to govern these complex systems, including mandatory
control reconfiguration to guarantee the fault tolerance characteristic. A new reconfiguration-less
approach based on virtual voltage vectors (VVs) was recently proposed for MPC, providing a natural
healthy and faulty closed-loop regulation of a particular asymmetrical six-phase drive. This work
validates the interest in the reconfiguration-less approach for direct controllers and multiphase drives
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