338 research outputs found
Electronic structure of superoxygenated La2NiO4 domains with ordered oxygen interstitials
The electronic structure of La2NiO4+d, where additional oxygen interstitials
are forming stripes along (1,1,0), are presented. Spin-polarized calculations
show that ferromagnetism on Ni sites is reduced near the stripes and enhanced
far from the stripes. Totally the magnetic moment becomes reduced because of
oxygen interstitials. It is suggested that the oxygen interstitial
concentration in oxygen rich domains in nickelates suppress magnetism and give
multiband metallic domains.Comment: 5 pages, 4 figure
The spread of fire on a random multigraph
We study a model for the destruction of a random network by fire. Suppose
that we are given a multigraph of minimum degree at least 2 having real-valued
edge-lengths. We pick a uniform point from along the length and set it alight;
the edges of the multigraph burn at speed 1. If the fire reaches a vertex of
degree 2, the fire gets directly passed on to the neighbouring edge; a vertex
of degree at least 3, however, passes the fire either to all of its neighbours
or none, each with probability . If the fire goes out before the whole
network is burnt, we again set fire to a uniform point. We are interested in
the number of fires which must be set in order to burn the whole network, and
the number of points which are burnt from two different directions. We analyse
these quantities for a random multigraph having vertices of degree 3 and
vertices of degree 4, where as ,
with i.i.d. standard exponential edge-lengths. Depending on whether or , we prove that as these
quantities converge jointly in distribution when suitably rescaled to either a
pair of constants or to (complicated) functionals of Brownian motion.
We use our analysis of this model to make progress towards a conjecture of
Aronson, Frieze and Pittel concerning the number of vertices which remain
unmatched when we use the Karp-Sipser algorithm to find a matching on the
Erd\H{o}s-R\'enyi random graph.Comment: 42 page
A new technique for simulating composite material. Task 2: Analytical solutions with Generalized Impedance Boundary Conditions (GIBCs)
The diffraction problem associated with a multilayer material slab recessed in a perfectly conducting ground plane is formulated and solved via the Generalized Scattering Matrix Formulation (GSMF) in conjunction with the dual integral equation approach. The multilayer slab is replaced by a surface obeying a generalized impedance boundary condition (GIBC) to facilitate the computation of the pertinent Wiener Hopf split functions and their zeros. Both E(sub z) and H(sub z) polarizations are considered and a number of scattering patterns are presented, some of which are compared to exact results available for a homogeneous recessed slab
Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise.
The hippocampus has been shown to demonstrate a remarkable degree of plasticity in response to a variety of tasks and experiences. For example, the size of the human hippocampus has been shown to increase in response to aerobic exercise. However, it is currently unknown what underlies these changes. Here we scanned sedentary, young to middle-aged human adults before and after a six-week exercise intervention using nine different neuroimaging measures of brain structure, vasculature, and diffusion. We then tested two different hypotheses regarding the nature of the underlying changes in the tissue. Surprisingly, we found no evidence of a vascular change as has been previously reported. Rather, the pattern of changes is better explained by an increase in myelination. Finally, we show hippocampal volume increase is temporary, returning to baseline after an additional six weeks without aerobic exercise. This is the first demonstration of a change in hippocampal volume in early to middle adulthood suggesting that hippocampal volume is modulated by aerobic exercise throughout the lifespan rather than only in the presence of age related atrophy. It is also the first demonstration of hippocampal volume change over a period of only six weeks, suggesting gross morphometric hippocampal plasticity occurs faster than previously thought
Identifying nonlinear wave interactions in plasmas using two-point measurements: a case study of Short Large Amplitude Magnetic Structures (SLAMS)
A framework is described for estimating Linear growth rates and spectral
energy transfers in turbulent wave-fields using two-point measurements. This
approach, which is based on Volterra series, is applied to dual satellite data
gathered in the vicinity of the Earth's bow shock, where Short Large Amplitude
Magnetic Structures (SLAMS) supposedly play a leading role. The analysis
attests the dynamic evolution of the SLAMS and reveals an energy cascade toward
high-frequency waves.Comment: 26 pages, 13 figure
Classification and estimation in the Stochastic Blockmodel based on the empirical degrees
International audienceThe Stochastic Blockmodel [16] is a mixture model for heterogeneous network data. Unlike the usual statistical framework, new nodes give additional information about the previous ones in this model. Thereby the distribution of the degrees concentrates in points conditionally on the node class. We show under a mild assumption that classification, estimation and model selection can actually be achieved with no more than the empirical degree data. We provide an algorithm able to process very large networks and consistent estimators based on it. In particular, we prove a bound of the probability of misclassification of at least one node, including when the number of classes grows
Community detection algorithms: a comparative analysis
Uncovering the community structure exhibited by real networks is a crucial
step towards an understanding of complex systems that goes beyond the local
organization of their constituents. Many algorithms have been proposed so far,
but none of them has been subjected to strict tests to evaluate their
performance. Most of the sporadic tests performed so far involved small
networks with known community structure and/or artificial graphs with a
simplified structure, which is very uncommon in real systems. Here we test
several methods against a recently introduced class of benchmark graphs, with
heterogeneous distributions of degree and community size. The methods are also
tested against the benchmark by Girvan and Newman and on random graphs. As a
result of our analysis, three recent algorithms introduced by Rosvall and
Bergstrom, Blondel et al. and Ronhovde and Nussinov, respectively, have an
excellent performance, with the additional advantage of low computational
complexity, which enables one to analyze large systems.Comment: 12 pages, 8 figures. The software to compute the values of our
general normalized mutual information is available at
http://santo.fortunato.googlepages.com/inthepress
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
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