340 research outputs found
Localization transition on complex networks via spectral statistics
The spectral statistics of complex networks are numerically studied.
The features of the Anderson metal-insulator transition are found to be
similar for a wide range of different networks. A metal-insulator transition as
a function of the disorder can be observed for different classes of complex
networks for which the average connectivity is small. The critical index of the
transition corresponds to the mean field expectation. When the connectivity is
higher, the amount of disorder needed to reach a certain degree of localization
is proportional to the average connectivity, though a precise transition cannot
be identified. The absence of a clear transition at high connectivity is
probably due to the very compact structure of the highly connected networks,
resulting in a small diameter even for a large number of sites.Comment: 6 pages, expanded introduction and referencess (to appear in PRE
Behavior of vortices near twin boundaries in underdoped
We use scanning SQUID microscopy to investigate the behavior of vortices in
the presence of twin boundaries in the pnictide superconductor
Ba(Fe1-xCox)2As2. We show that the vortices avoid pinning on twin boundaries.
Individual vortices move in a preferential way when manipulated with the SQUID:
they tend to not cross a twin boundary, but rather to move parallel to it. This
behavior can be explained by the observation of enhanced superfluid density on
twin boundaries in Ba(Fe1-xCox)2As2. The observed repulsion from twin
boundaries may be a mechanism for enhanced critical currents observed in
twinned samples in pnictides and other superconductors
Volatility of Linear and Nonlinear Time Series
Previous studies indicate that nonlinear properties of Gaussian time series
with long-range correlations, , can be detected and quantified by studying
the correlations in the magnitude series , i.e., the ``volatility''.
However, the origin for this empirical observation still remains unclear, and
the exact relation between the correlations in and the correlations in
is still unknown. Here we find analytical relations between the scaling
exponent of linear series and its magnitude series . Moreover, we
find that nonlinear time series exhibit stronger (or the same) correlations in
the magnitude time series compared to linear time series with the same
two-point correlations. Based on these results we propose a simple model that
generates multifractal time series by explicitly inserting long range
correlations in the magnitude series; the nonlinear multifractal time series is
generated by multiplying a long-range correlated time series (that represents
the magnitude series) with uncorrelated time series [that represents the sign
series ]. Our results of magnitude series correlations may help to
identify linear and nonlinear processes in experimental records.Comment: 7 pages, 5 figure
Scanning SQUID Susceptometry of a paramagnetic superconductor
Scanning SQUID susceptometry images the local magnetization and
susceptibility of a sample. By accurately modeling the SQUID signal we can
determine the physical properties such as the penetration depth and
permeability of superconducting samples. We calculate the scanning SQUID
susceptometry signal for a superconducting slab of arbitrary thickness with
isotropic London penetration depth, on a non-superconducting substrate, where
both slab and substrate can have a paramagnetic response that is linear in the
applied field. We derive analytical approximations to our general expression in
a number of limits. Using our results, we fit experimental susceptibility data
as a function of the sample-sensor spacing for three samples: 1) delta-doped
SrTiO3, which has a predominantly diamagnetic response, 2) a thin film of
LaNiO3, which has a predominantly paramagnetic response, and 3) a
two-dimensional electron layer (2-DEL) at a SrTiO3/AlAlO3 interface, which
exhibits both types of response. These formulas will allow the determination of
the concentrations of paramagnetic spins and superconducting carriers from fits
to scanning SQUID susceptibility measurements.Comment: 11 pages, 13 figure
Stromal Gli2 activity coordinates a niche signaling program for mammary epithelial stem cells
The stem cell niche is a complex local signaling microenvironment that sustains stem cell activity during organ maintenance and regeneration. The mammary gland niche must support its associated stem cells while also responding to systemic hormonal regulation that triggers pubertal changes. We find that Gli2, the major Hedgehog pathway transcriptional effector, acts within mouse mammary stromal cells to direct a hormoneresponsive niche signaling program by activating expression of factors that regulate epithelial stem cells as well as receptors for the mammatrophic hormones estrogen and growth hormone.Whereas prior studies implicate stem cell defects in human disease, this work shows that niche dysfunction may also cause disease, with possible relevance for human disorders and in particular the breast growth pathogenesis associated with combined pituitary hormone deficiency. Copyright 2016 by the American Association for the Advancement of Science, all rights reserved.116Ysciescopu
Limits on Relief through Constrained Exchange on Random Graphs
Agents are represented by nodes on a random graph (e.g., small world or
truncated power law). Each agent is endowed with a zero-mean random value that
may be either positive or negative. All agents attempt to find relief, i.e., to
reduce the magnitude of that initial value, to zero if possible, through
exchanges. The exchange occurs only between agents that are linked, a
constraint that turns out to dominate the results. The exchange process
continues until a Pareto equilibrium is achieved. Only 40%-90% of the agents
achieved relief on small world graphs with mean degree between 2 and 40. Even
fewer agents achieved relief on scale-free like graphs with a truncated power
law degree distribution. The rate at which relief grew with increasing degree
was slow, only at most logarithmic for all of the graphs considered; viewed in
reverse, relief is resilient to the removal of links.Comment: 8 pages, 2 figures, 22 references Changes include name change for
Lory A. Ellebracht (formerly Cooperstock, e-mail address stays the same),
elimination of contractions and additional references. We also note that our
results are less surprising in view of other work now cite
Effect of Disorder Strength on Optimal Paths in Complex Networks
We study the transition between the strong and weak disorder regimes in the
scaling properties of the average optimal path in a disordered
Erd\H{o}s-R\'enyi (ER) random network and scale-free (SF) network. Each link
is associated with a weight , where is a
random number taken from a uniform distribution between 0 and 1 and the
parameter controls the strength of the disorder. We find that for any
finite , there is a crossover network size at which the transition
occurs. For the scaling behavior of is in the
strong disorder regime, with for ER networks and
for SF networks with , and for SF networks with . For the scaling behavior is in the weak disorder regime, with for ER networks and SF networks with . In order to
study the transition we propose a measure which indicates how close or far the
disordered network is from the limit of strong disorder. We propose a scaling
ansatz for this measure and demonstrate its validity. We proceed to derive the
scaling relation between and . We find that for ER
networks and for SF networks with , and for SF networks with .Comment: 6 pages, 6 figures. submitted to Phys. Rev.
Optimal Path and Minimal Spanning Trees in Random Weighted Networks
We review results on the scaling of the optimal path length in random
networks with weighted links or nodes. In strong disorder we find that the
length of the optimal path increases dramatically compared to the known small
world result for the minimum distance. For Erd\H{o}s-R\'enyi (ER) and scale
free networks (SF), with parameter (), we find that the
small-world nature is destroyed. We also find numerically that for weak
disorder the length of the optimal path scales logaritmically with the size of
the networks studied. We also review the transition between the strong and weak
disorder regimes in the scaling properties of the length of the optimal path
for ER and SF networks and for a general distribution of weights, and suggest
that for any distribution of weigths, the distribution of optimal path lengths
has a universal form which is controlled by the scaling parameter
where plays the role of the disorder strength, and
is the length of the optimal path in strong disorder. The
relation for is derived analytically and supported by numerical
simulations. We then study the minimum spanning tree (MST) and show that it is
composed of percolation clusters, which we regard as "super-nodes", connected
by a scale-free tree. We furthermore show that the MST can be partitioned into
two distinct components. One component the {\it superhighways}, for which the
nodes with high centrality dominate, corresponds to the largest cluster at the
percolation threshold which is a subset of the MST. In the other component,
{\it roads}, low centrality nodes dominate. We demonstrate the significance
identifying the superhighways by showing that one can improve significantly the
global transport by improving a very small fraction of the network.Comment: review, accepted at IJB
Surface superconductivity in multilayered rhombohedral graphene: Supercurrent
The supercurrent for the surface superconductivity of a flat-band
multilayered rhombohedral graphene is calculated. Despite the absence of
dispersion of the excitation spectrum, the supercurrent is finite. The critical
current is proportional to the zero-temperature superconducting gap, i.e., to
the superconducting critical temperature and to the size of the flat band in
the momentum space
In silico evolution of diauxic growth
The glucose effect is a well known phenomenon whereby cells, when presented with two different nutrients, show a diauxic growth pattern, i.e. an episode of exponential growth followed by a lag phase of reduced growth followed by a second phase of exponential growth. Diauxic growth is usually thought of as a an adaptation to maximise biomass production in an environment offering two or more carbon sources. While diauxic growth has been studied widely both experimentally and theoretically, the hypothesis that diauxic growth is a strategy to increase overall growth has remained an unconfirmed conjecture. Here, we present a minimal mathematical model of a bacterial nutrient uptake system and metabolism. We subject this model to artificial evolution to test under which conditions diauxic growth evolves. As a result, we find that, indeed, sequential uptake of nutrients emerges if there is competition for nutrients and the metabolism/uptake system is capacity limited. However, we also find that diauxic growth is a secondary effect of this system and that the speed-up of nutrient uptake is a much larger effect. Notably, this speed-up of nutrient uptake coincides with an overall reduction of efficiency. Our two main conclusions are: (i) Cells competing for the same nutrients evolve rapid but inefficient growth dynamics. (ii) In the deterministic models we use here no substantial lag-phase evolves. This suggests that the lag-phase is a consequence of stochastic gene expression
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