"Prices for Paintings by African American Artists and Their Contemporaries: Does Race Matter? (Revision of Working Paper No. 2006-06)"

This paper investigates the extent that economic markets have incorporated mainstream artistic acceptance of African American art. Price levels and movements for paintings by African American artists versus their white contemporaries are compared using auction data from 1972 to 2004. Means in the aggregate as well as individually are found to be significantly lower for African American artists in almost every case. Hedonic regressions are used to refine the statistical analysis by controlling for factors characterizing the painting and auction environment. In the regressions significant differences persist between the two groups with African American artists experiencing lower price levels but higher price appreciation throughout the period. The price gap thus appears to be narrowing indicating a possible convergence of economic reality and artistic appreciation. In addition, the higher investment returns for paintings by African American artists made them a relatively profitable art niche in recent years and possibly for the future since economic values have not completely converged for the two groups.Economics of Art, Painting Prices, African American Painters, Hedonic Regression

Bio-inspired hydrogels as multi-task anti-icing hydrogel coatings

In a recent report in Matter, Zhu, Wang, He and co-workers report a straightforward and effective strategy for the design of icephobic hydrogel coatings on the basis of polydimethylsiloxane (PDMS)-grafted polyelectrolyte hydrogels. These passive anti-icing and de-icing coatings were demonstrated to synergistically suppress ice nucleation, ice propagation, and ice adhesion

Self-healing metallo-supramolecular hydrogel based on specific Ni2+ coordination interactions of poly(ethylene glycol) with bistriazole pyridine ligands in the main chain

In this study, a supramolecular hydrogel formed by incorporating the 2,6-bis(1,2,3-triazol-4-yl)-pyridine (btp) ligand in the backbone of a polymer prepared by copper(I)-catalyzed alkyne-azide cycloaddition (CuAAC) "click" polyaddition reaction of 2,6-diethynylpyridine and diazido-poly(ethylene glycol) is reported. The hydrogelation is selectively triggered by the addition of Ni2+ ions to aqueous copolymer solutions. The gelation and rheological properties could be tuned by the change of metal to ligand ratio and polymer concentration. Interestingly, the hydrogel exhibits a fast (within 2 min) and excellent repeatable autonomic healing capacity without external stimuli. This self-healing behavior may find potential applications for the repairing of metal coatings, in the future

Structural diversification of pillar[n]arene macrocycles

Despite the fact that pillar[n]arenes receive major interest as building blocks for supramolecular chemistry and advanced materials, their functionalization is generally limited to the modification of the hydroxy or alkoxy units present on the rims. This limited structural freedom restricts further developments and has very recently been overcome. In this article, we highlight three very recent studies demonstrating further structural diversification of pillar[n]arenes by partial removal of the alkoxy substituents on the rims, which can be considered as the next generation of pillar[n]arenes

Information Splitting for Big Data Analytics

Many statistical models require an estimation of unknown (co)-variance parameter(s) in a model. The estimation usually obtained by maximizing a log-likelihood which involves log determinant terms. In principle, one requires the \emph{observed information}--the negative Hessian matrix or the second derivative of the log-likelihood---to obtain an accurate maximum likelihood estimator according to the Newton method. When one uses the \emph{Fisher information}, the expect value of the observed information, a simpler algorithm than the Newton method is obtained as the Fisher scoring algorithm. With the advance in high-throughput technologies in the biological sciences, recommendation systems and social networks, the sizes of data sets---and the corresponding statistical models---have suddenly increased by several orders of magnitude. Neither the observed information nor the Fisher information is easy to obtained for these big data sets. This paper introduces an information splitting technique to simplify the computation. After splitting the mean of the observed information and the Fisher information, an simpler approximate Hessian matrix for the log-likelihood can be obtained. This approximated Hessian matrix can significantly reduce computations, and makes the linear mixed model applicable for big data sets. Such a spitting and simpler formulas heavily depends on matrix algebra transforms, and applicable to large scale breeding model, genetics wide association analysis.Comment: arXiv admin note: text overlap with arXiv:1605.0764