Decoupling method for dynamical mean field theory calculations

In this paper we explore the use of an equation of motion decoupling method as an impurity solver to be used in conjunction with the dynamical mean field self-consistency condition for the solution of lattice models. We benchmark the impurity solver against exact diagonalization, and apply the method to study the infinite $U$ Hubbard model, the periodic Anderson model and the $pd$ model. This simple and numerically efficient approach yields the spectra expected for strongly correlated materials, with a quasiparticle peak and a Hubbard band. It works in a large range of parameters, and therefore can be used for the exploration of real materials using LDA+DMFT.Comment: 30 pages, 7 figure

Ancillary Joint Ventures and the Unanswered Questions after Revenue Ruling 2004-51

Ever since the Internal Revenue Service (the Service ) issued Revenue Ruling 98-15… in which it emphasized control as a critical factor in determining whether a tax-exempt hospital that enters into a whole-hospital joint venture with a for-profit entity would continue to maintain its tax-exemption, practitioners and scholars alike have sought guidance from the Service regarding whether such control would also be required of an exempt organization that enters into an ancillary joint venture with a for-profit entity. In response, the Service issued Revenue Ruling 2004-51 on May 6, 2004. … In Revenue Ruling 2004-51, the Service enunciated that a tax-exempt university that formed a joint venture with a for-profit entity by contributing a portion of its assets to, and conducting a portion of its activities through, the joint venture would neither lose its tax exemption nor be subject to unrelated business income tax (UBIT) on its share of income from the joint venture because (the facts state that) the tax-exempt university\u27s activities conducted through the joint venture are not a substantial part of … [the tax-exempt university\u27s] activities within the meaning of § 501(c)(3) and § 1.501(c)(3)-1(c)(1) … and the activities of the joint venture are substantially related to the university\u27s exempt purpose. … Regrettably, however, the Service failed to provide any guidance on how it determined that the assets and activities of the exempt university conducted through the joint venture are not a substantial part of the exempt university\u27s activities. … Such a conclusive disposition of a key element of determining tax exemption within the ancillary joint venture context is puzzling, and fans the embers of ambiguity, because it fails to provide any quantitative or qualitative guidance, or safe harbor tests, for determining when the assets and activities of a tax-exempt organization that are transferred to, and conducted through, a joint venture are considered not a substantial part of the exempt organization\u27s activities within the meaning of I.R.C. §501(c)(3) and Treas. Reg. §1.501(c)(3)-1(c)(1) so as not to jeopardize the organization\u27s continued tax exemption… … Moreover, the Service\u27s conclusion that based on all the facts and circumstances, the tax-exempt university\u27s participation in the joint venture taken alone, will not affect its continued qualification for tax exemption is not unequivocal in many respects. … The phrase taken alone could be interpreted as suggesting that ancillary joint venture activities of an exempt organization which may not ordinarily result in the loss of tax exemption (because such activities are not considered a substantial part of the organization\u27s activities when viewed separately) may indeed impair tax exemption if, in the aggregate, such activities constitute a substantial part of the exempt organization\u27s activities. … To provide clarity to the rules of federal tax exemption within the context of ancillary joint ventures, the Service needs to issue a new ruling clarifying revenue ruling 2004-51 and establishing safe harbor provisions for determining when the assets transferred to, and activities conducted through, a joint venture by a tax-exempt organization would be presumed not a substantial part of the exempt organization\u27s assets and activities so as not to jeopardize its tax exemption within the meaning of I.R.C. §501(c)(3) and Treas. Reg. § 1.501(c)(3)-1(c)(1)

How good a map ? Putting small area estimation to the test

The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for a set of target populations with their true values. They construct target populations using actual data from a census of households in a set of rural Mexican communities. They examine estimates along three criteria: accuracy of confidence intervals, bias, and correlation with true values. The authors find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. While the original approach of numerical gradient estimation yields standard errors that seem appropriate, some computationally less-intensive simulation procedures yield confidence intervals that are slightly too narrow. The precision of estimates is shown to diminish markedly if unobserved location effects at the village level are not well captured in underlying consumption models. With well specified models there is only slight evidence of bias, but the authors show that bias increases if underlying models fail to capture latent location effects. Correlations between estimated and true welfare at the local level are highest for mean expenditure and poverty measures and lower for inequality measures.Small Area Estimation Poverty Mapping,Rural Poverty Reduction,Science Education,Scientific Research&Science Parks,Population Policies

Essential Constraints of Edge-Constrained Proximity Graphs

Given a plane forest $F = (V, E)$ of $|V| = n$ points, we find the minimum set $S \subseteq E$ of edges such that the edge-constrained minimum spanning tree over the set $V$ of vertices and the set $S$ of constraints contains $F$. We present an $O(n \log n )$-time algorithm that solves this problem. We generalize this to other proximity graphs in the constraint setting, such as the relative neighbourhood graph, Gabriel graph, $\beta$-skeleton and Delaunay triangulation. We present an algorithm that identifies the minimum set $S\subseteq E$ of edges of a given plane graph $I=(V,E)$ such that $I \subseteq CG_\beta(V, S)$ for $1 \leq \beta \leq 2$, where $CG_\beta(V, S)$ is the constraint $\beta$-skeleton over the set $V$ of vertices and the set $S$ of constraints. The running time of our algorithm is $O(n)$, provided that the constrained Delaunay triangulation of $I$ is given.Comment: 24 pages, 22 figures. A preliminary version of this paper appeared in the Proceedings of 27th International Workshop, IWOCA 2016, Helsinki, Finland. It was published by Springer in the Lecture Notes in Computer Science (LNCS) serie