117,268 research outputs found
Effective Magnetic Fields in Graphene Superlattices
We demonstrate that the electronic spectrum of graphene in a one-dimensional
periodic potential will develop a Landau level spectrum when the potential
magnitude varies slowly in space. The effect is related to extra Dirac points
generated by the potential whose positions are sensitive to its magnitude. We
develop an effective theory that exploits a chiral symmetry in the Dirac
Hamiltonian description with a superlattice potential, to show that the low
energy theory contains an effective magnetic field. Numerical diagonalization
of the Dirac equation confirms the presence of Landau levels. Possible
consequences for transport are discussed.Comment: 4 pages (+ 2 pages of supplementary material), 3 figure
Entanglement between two fermionic atoms inside a cylindrical harmonic trap
We investigate quantum entanglement between two (spin-1/2) fermions inside a
cylindrical harmonic trap, making use of the von Neumann entropy for the
reduced single particle density matrix as the pure state entanglement measure.
We explore the dependence of pair entanglement on the geometry and strength of
the trap and on the strength of the pairing interaction over the complete range
of the effective BCS to BEC crossover. Our result elucidates an interesting
connection between our model system of two fermions and that of two interacting
bosons.Comment: to appear in PR
Distribution of extremes in the fluctuations of two-dimensional equilibrium interfaces
We investigate the statistics of the maximal fluctuation of two-dimensional
Gaussian interfaces. Its relation to the entropic repulsion between rigid walls
and a confined interface is used to derive the average maximal fluctuation and the asymptotic behavior of the whole
distribution for finite with and the interface size and
tension, respectively. The standardized form of does not depend on
or , but shows a good agreement with Gumbel's first asymptote distribution
with a particular non-integer parameter. The effects of the correlations among
individual fluctuations on the extreme value statistics are discussed in our
findings.Comment: 4 pages, 4 figures, final version in PR
Zero-field magnetization reversal of two-body Stoner particles with dipolar interaction
Nanomagnetism has recently attracted explosive attention, in particular,
because of the enormous potential applications in information industry, e.g.
new harddisk technology, race-track memory[1], and logic devices[2]. Recent
technological advances[3] allow for the fabrication of single-domain magnetic
nanoparticles (Stoner particles), whose magnetization dynamics have been
extensively studied, both experimentally and theoretically, involving magnetic
fields[4-9] and/or by spin-polarized currents[10-20]. From an industrial point
of view, important issues include lowering the critical switching field ,
and achieving short reversal times. Here we predict a new technological
perspective: can be dramatically lowered (including ) by
appropriately engineering the dipole-dipole interaction (DDI) in a system of
two synchronized Stoner particles. Here, in a modified Stoner-Wohlfarth (SW)
limit, both of the above goals can be achieved. The experimental feasibility of
realizing our proposal is illustrated on the example of cobalt nanoparticles.Comment: 5 pages, 4 figure
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
While the use of bottom-up local operators in convolutional neural networks
(CNNs) matches well some of the statistics of natural images, it may also
prevent such models from capturing contextual long-range feature interactions.
In this work, we propose a simple, lightweight approach for better context
exploitation in CNNs. We do so by introducing a pair of operators: gather,
which efficiently aggregates feature responses from a large spatial extent, and
excite, which redistributes the pooled information to local features. The
operators are cheap, both in terms of number of added parameters and
computational complexity, and can be integrated directly in existing
architectures to improve their performance. Experiments on several datasets
show that gather-excite can bring benefits comparable to increasing the depth
of a CNN at a fraction of the cost. For example, we find ResNet-50 with
gather-excite operators is able to outperform its 101-layer counterpart on
ImageNet with no additional learnable parameters. We also propose a parametric
gather-excite operator pair which yields further performance gains, relate it
to the recently-introduced Squeeze-and-Excitation Networks, and analyse the
effects of these changes to the CNN feature activation statistics.Comment: NeurIPS 201
Energy Gap Induced by Impurity Scattering: New Phase Transition in Anisotropic Superconductors
It is shown that layered superconductors are subjected to a phase transition
at zero temperature provided the order parameter (OP) reverses its sign on the
Fermi-surface but its angular average is finite. The transition is regulated by
an elastic impurity scattering rate . The excitation energy spectrum,
being gapless at the low level of scattering, develops a gap as soon as the
scattering rate exceeds some critical value of .Comment: Revtex, 11 page
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