930 research outputs found
Finance and Income Inequality: What Do the Data Tell Us?
Although there are distinct conjectures about the relationship between finance and income inequality, little empirical research compares their explanatory power. We examine the relationship between finance and income inequality for 83 countries between 1960 and 1995. Because financial development might be endogenous, we use instruments from the literature on law, finance, and growth to control for this. Our results suggest that, in the long run, inequality is less when financial development is greater, consistent with Galor and Zeira (1993) and Banerjee and Newman (1993). Although the results also suggest that inequality might increase as financial sector development increases at very low levels of financial sector development, as suggested by Greenwood and Jovanovic (1990), this result is not robust. We reject the hypothesis that financial development benefits only the rich. Our results thus suggest that in addition to improving growth, financial development also reduces inequality.
First-principles study of multiferroic RbFe(MoO)
We have investigated the magnetic structure and ferroelectricity in
RbFe(MoO) via first-principles calculations. Phenomenological analyses
have shown that ferroelectricity may arise due to both the triangular chirality
of the magnetic structure, and through coupling between the magnetic helicity
and the ferroaxial structural distortion. Indeed, it was recently proposed that
the structural distortion plays a key role in stabilising the chiral magnetic
structure itself. We have determined the relative contribution of the two
mechanisms via \emph{ab-initio} calculations. Whilst the structural axiality
does induce the magnetic helix by modulating the symmetric exchange
interactions, the electric polarization is largely due to the in-plane spin
triangular chirality, with both electronic and ionic contributions being of
relativistic origin. At the microscopic level, we interpret the polarization as
a secondary steric consequence of the inverse Dzyaloshinskii-Moriya mechanism
and accordingly explain why the ferroaxial component of the electric
polarization must be small
Cosmic Constraint to DGP Brane Model: Geometrical and Dynamical Perspectives
In this paper, the Dvali-Gabadadze-Porrati (DGP) brane model is confronted by
current cosmic observational data sets from geometrical and dynamical
perspectives. On the geometrical side, the recent released Union2 of type
Ia supernovae (SN Ia), the baryon acoustic oscillation (BAO) from Sloan Digital
Sky Survey and the Two Degree Galaxy Redshift Survey (transverse and radial to
line-of-sight data points), the cosmic microwave background (CMB) measurement
given by the seven-year Wilkinson Microwave Anisotropy Probe observations
(shift parameters , and redshift at the last scatter surface
), ages of high redshifts galaxies, i.e. the lookback time (LT) and the
high redshift Gamma Ray Bursts (GRBs) are used. On the dynamical side, data
points about the growth function (GF) of matter linear perturbations are used.
Using the same data sets combination, we also constrain the flat CDM
model as a comparison. The results show that current geometrical and dynamical
observational data sets much favor flat CDM model and the departure
from it is above () for spatially flat DGP model
with(without) SN systematic errors. The consistence of growth function data
points is checked in terms of relative departure of redshift-distance relation.Comment: 14 pages, 5 figures, 2 tables, accepted for publication in Physical
Review
QESK: Quantum-based Entropic Subtree Kernels for Graph Classification
In this paper, we propose a novel graph kernel, namely the Quantum-based
Entropic Subtree Kernel (QESK), for Graph Classification. To this end, we
commence by computing the Average Mixing Matrix (AMM) of the Continuous-time
Quantum Walk (CTQW) evolved on each graph structure. Moreover, we show how this
AMM matrix can be employed to compute a series of entropic subtree
representations associated with the classical Weisfeiler-Lehman (WL) algorithm.
For a pair of graphs, the QESK kernel is defined by computing the
exponentiation of the negative Euclidean distance between their entropic
subtree representations, theoretically resulting in a positive definite graph
kernel. We show that the proposed QESK kernel not only encapsulates complicated
intrinsic quantum-based structural characteristics of graph structures through
the CTQW, but also theoretically addresses the shortcoming of ignoring the
effects of unshared substructures arising in state-of-the-art R-convolution
graph kernels. Moreover, unlike the classical R-convolution kernels, the
proposed QESK can discriminate the distinctions of isomorphic subtrees in terms
of the global graph structures, theoretically explaining the effectiveness.
Experiments indicate that the proposed QESK kernel can significantly outperform
state-of-the-art graph kernels and graph deep learning methods for graph
classification problems
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
In this paper, we develop a new graph kernel, namely the Hierarchical
Transitive-Aligned kernel, by transitively aligning the vertices between graphs
through a family of hierarchical prototype graphs. Comparing to most existing
state-of-the-art graph kernels, the proposed kernel has three theoretical
advantages. First, it incorporates the locational correspondence information
between graphs into the kernel computation, and thus overcomes the shortcoming
of ignoring structural correspondences arising in most R-convolution kernels.
Second, it guarantees the transitivity between the correspondence information
that is not available for most existing matching kernels. Third, it
incorporates the information of all graphs under comparisons into the kernel
computation process, and thus encapsulates richer characteristics. By
transductively training the C-SVM classifier, experimental evaluations
demonstrate the effectiveness of the new transitive-aligned kernel. The
proposed kernel can outperform state-of-the-art graph kernels on standard
graph-based datasets in terms of the classification accuracy
Correspondence Between DGP Brane Cosmology and 5D Ricci-flat Cosmology
We discuss the correspondence between the DGP brane cosmology and 5D
Ricci-flat cosmology by letting their metrics equal each other. By this
correspondence, a specific geometrical property of the arbitrary integral
constant I in DGP metric is given and it is related to the curvature of 5D
bulk. At the same time, the relation of arbitrary functions and in
a class of Ricci-flat solutions is obtained from DGP brane metric.Comment: 8 pages, 1 figure, accepted by MPLA, added referenc
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