9,797 research outputs found
Investigation of the Interior of Colored Black Holes and the Extendability of Solutions of the Einstein-Yang/Mills Equations
We prove that any asymptotically flat solution to the spherically symmetric
SU(2) Einstein-Yang/Mills equations is globally defined. This result applies in
particular to the interior of colored black holes.Comment: Latex, 8 gif figure
Social Requirements for Virtual Organization Breeding Environments
The creation of Virtual Breeding Environments (VBE) is a topic which has
received too little attention: in most former works, the existence of the VBE
is either assumed, or is considered as the result of the voluntary,
participatory gathering of a set of candidate companies. In this paper, the
creation of a VBE by a third authority is considered: chambers of commerce, as
organizations whose goal is to promote and facilitate business interests and
activity in the community, could be good candidates for exogenous VBE creators.
During VBE planning, there is a need to specify social requirements for the
VBE. In this paper, SNA metrics are proposed as a way for a VBE planner to
express social requirements for a VBE to be created. Additionally, a set of
social requirements for VO planners, VO brokers, and VBE members are proposed.Comment: 10 pages, 2 figure
Reissner-Nordstrom-like solutions of the SU(2) Einstein-Yang/Mills (EYM) equations
In this paper we study a new type of solution of the spherically symmetric,
Einstein-Yang/Mills (EYM) equations with SU(2) gauge group. These solutions are
well-behaved in the far-field, and have a Reissner-Nordstrom type essential
singularity at the origin. These solutions display some novel features which
are not present in particle-like, or black-hole solutions. Any spherically
symmetric solution to the EYM equations, defined in the far-field, is either a
particle-like solution, a black-hole solution, or one of these RNL solutions.Comment: 5 pages, latex, no figures, Submitted to Comm. Math. Phys. January
15, 199
Strong absorption and selective thermal emission from a mid-infrared metamaterial
We demonstrate thin-film metamaterials with resonances in the mid-infrared
wavelength range. Our structures are numerically modeled and experimentally
characterized by reflection and angularly-resolved thermal emission
spectroscopy. We demonstrate strong and controllable absorption resonances
across the mid-infrared wavelength range. In addition, the polarized thermal
emission from these samples is shown to be highly selective and largely
independent of emission angles from normal to 45 degrees. Experimental results
are compared to numerical models with excellent agreement. Such structures hold
promise for large-area, low-cost metamaterial coatings for control of gray- or
black-body thermal signatures, as well as for possible mid-IR sensing
applications.Comment: The following article has been submitted to Appl. Phys. Lett. After
it is published, it will be found at http://apl.aip.org/. 14 pages including
4 figure page
The Pharmacology of Newer Diuretics
There are now a number of different classes of diuretics with different pharmacologic effects. Several considerations dictate the choice of diuretic: 1. The responsiveness of the patient is of prime importance. If the patient is not known to be resistant to diuretic therapy, thiazides should be tried first. 2. The danger of alterations of volume and of electrolytes in the specific patient must be considered. Patients receiving digitalis will be subjected to much greater danger by the induction of hypokalemia than patients not receiving cardiac glycosides. 3. The pharmacologic effects of the specific diuretics must be understood for now the physician has available agents of differing potency, efficacy, and especially differing mechanisms of action
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives
Nonparametric two sample testing deals with the question of consistently
deciding if two distributions are different, given samples from both, without
making any parametric assumptions about the form of the distributions. The
current literature is split into two kinds of tests - those which are
consistent without any assumptions about how the distributions may differ
(\textit{general} alternatives), and those which are designed to specifically
test easier alternatives, like a difference in means (\textit{mean-shift}
alternatives).
The main contribution of this paper is to explicitly characterize the power
of a popular nonparametric two sample test, designed for general alternatives,
under a mean-shift alternative in the high-dimensional setting. Specifically,
we explicitly derive the power of the linear-time Maximum Mean Discrepancy
statistic using the Gaussian kernel, where the dimension and sample size can
both tend to infinity at any rate, and the two distributions differ in their
means. As a corollary, we find that if the signal-to-noise ratio is held
constant, then the test's power goes to one if the number of samples increases
faster than the dimension increases. This is the first explicit power
derivation for a general nonparametric test in the high-dimensional setting,
and also the first analysis of how tests designed for general alternatives
perform when faced with easier ones.Comment: 25 pages, 5 figure
On the Decreasing Power of Kernel and Distance based Nonparametric Hypothesis Tests in High Dimensions
This paper is about two related decision theoretic problems, nonparametric
two-sample testing and independence testing. There is a belief that two
recently proposed solutions, based on kernels and distances between pairs of
points, behave well in high-dimensional settings. We identify different sources
of misconception that give rise to the above belief. Specifically, we
differentiate the hardness of estimation of test statistics from the hardness
of testing whether these statistics are zero or not, and explicitly discuss a
notion of "fair" alternative hypotheses for these problems as dimension
increases. We then demonstrate that the power of these tests actually drops
polynomially with increasing dimension against fair alternatives. We end with
some theoretical insights and shed light on the \textit{median heuristic} for
kernel bandwidth selection. Our work advances the current understanding of the
power of modern nonparametric hypothesis tests in high dimensions.Comment: 19 pages, 9 figures, published in AAAI-15: The 29th AAAI Conference
on Artificial Intelligence (with author order reversed from ArXiv
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