81,157 research outputs found
BoRa Jeong, Violin
Sonata for Piano and Violin No. 2 in G, Op. 13 / Edvard Grieg; String Duo for Violin and Viola in G major, K. 423 / W.A. Mozart; Liebesleid / Fritz Kreisler; Liebesfreud / Fritz Kreisle
Annie Hyojeong Jeong, Violin
Sonata for Piano and Violin in G Major, K. 301 / Wolfgang Amadeus Mozart; Solo Violin Partita No. 3 in E Major, BWV 1006 / Johann Sebastian Bach; Violin Concerto in D Major, Op 35 / Pyotr Ilyich Tchaikovsk
Direct observation of the formation of polar nanoregions in Pb(MgNb)O using neutron pair distribution function analysis
Using neutron pair distribution function (PDF) analysis over the temperature
range from 1000 K to 15 K, we demonstrate the existence of local polarization
and the formation of medium-range, polar nanoregions (PNRs) with local
rhombohedral order in a prototypical relaxor ferroelectric
Pb(MgNb)O. We estimate the volume fraction of the PNRs as a
function of temperature and show that this fraction steadily increases from 0 %
to a maximum of 30% as the temperature decreases from 650 K to 15 K.
Below T200 K the PNRs start to overlap as their volume fraction reaches
the percolation threshold. We propose that percolating PNRs and their
concomitant overlap play a significant role in the relaxor behavior of
Pb(MgNb)O.Comment: 4 pages, 3 figure
Diffusive Capture Process on Complex Networks
We study the dynamical properties of a diffusing lamb captured by a diffusing
lion on the complex networks with various sizes of . We find that the life
time and the survival probability becomes finite on scale-free networks with degree exponent
. However, for has a long-living tail on
tree-structured scale-free networks and decays exponentially on looped
scale-free networks. It suggests that the second moment of degree distribution
kn(k)n(k)\sim k^{-\sigma}\gamma<3n(k)k\approx k_{max}n(k)n(k)\sim k^2P(k)N_{tot}, which
causes the dependent behavior of and $.Comment: 9 pages, 6 figure
Scale-free networks with tunable degree distribution exponents
We propose and study a model of scale-free growing networks that gives a
degree distribution dominated by a power-law behavior with a model-dependent,
hence tunable, exponent. The model represents a hybrid of the growing networks
based on popularity-driven and fitness-driven preferential attachments. As the
network grows, a newly added node establishes new links to existing nodes
with a probability based on popularity of the existing nodes and a
probability based on fitness of the existing nodes. An explicit form of
the degree distribution is derived within a mean field approach. For
reasonably large , , where the
function is dominated by the behavior of for small
values of and becomes -independent as , and is a
model-dependent exponent. The degree distribution and the exponent
are found to be in good agreement with results obtained by extensive numerical
simulations.Comment: 12 pages, 2 figures, submitted to PR
Reverse engineering of linking preferences from network restructuring
We provide a method to deduce the preferences governing the restructuring
dynamics of a network from the observed rewiring of the edges. Our approach is
applicable for systems in which the preferences can be formulated in terms of a
single-vertex energy function with f(k) being the contribution of a node of
degree k to the total energy, and the dynamics obeys the detailed balance. The
method is first tested by Monte-Carlo simulations of restructuring graphs with
known energies, then it is used to study variations of real network systems
ranging from the co-authorship network of scientific publications to the asset
graphs of the New York Stock Exchange. The empirical energies obtained from the
restructuring can be described by a universal function f(k) -k ln(k), which is
consistent with and justifies the validity of the preferential attachment rule
proposed for growing networks.Comment: 7 pages, 6 figures, submitted to PR
- …