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(MoO4_4)2_2

We have investigated the magnetic structure and ferroelectricity in RbFe(MoO$_4$)$_2$ 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 $557$ 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 $R$, $l_a(z_\ast)$ and redshift at the last scatter surface $z_\ast$), 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 $\Lambda$CDM model as a comparison. The results show that current geometrical and dynamical observational data sets much favor flat $\Lambda$CDM model and the departure from it is above $4\sigma$($6\sigma$) 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 $\mu$ and $\nu$ in a class of Ricci-flat solutions is obtained from DGP brane metric.Comment: 8 pages, 1 figure, accepted by MPLA, added referenc