Boundary Regularity of Rotating Vortex Patches

We show that the boundary of a rotating vortex patch (or V-state, in the terminology of Deem and Zabusky) is C ^∞, provided the patch is close to the bifurcation circle in the Lipschitz norm. The rotating patch is also convex if it is close to the bifurcation circle in the C ² norm. Our proof is based on Burbea’s approach to V-states.     

The Latent Scale Covariogram: A Tool for Exploring the Spatial Dependence Structure of Nonnormal Responses

When modeling spatially distributed normal responses Y_i in terms of vectors x_i of explanatory variables, one may fit a linear model assuming independence, and then use the empirical variogram of the residuals to determine an appropriate parametric form for the autocorrelation function. Suppose, however, that the responses are not normally distributed—for example, Poisson or Bernoulli. One may model spatial dependence using a hierarchical generalized linear model in which, conditional on a latent Gaussian field Z = {Z_i}, the Y_i have independent distributions from the exponential family, with an appropriate link function connecting their conditional means with the linear predictors x^t_iβ + Z_i. The question then is how to determine an appropriate model for the autocorrelation function of Z. The empirical variogram of the Y_i is no longer appropriate, since (unless the link function is the identity) it is on the wrong scale. We propose here an alternative, the latent scale covariogram, whose graph reflects the autocorrelation structure of the underlying normal field. We illustrate its use on several real datasets, together with a simulated dataset, and obtain results quite different from those obtained using the variogram. Supplementary materials for this article are available online.     

Stochastic Order and Generalized Weighted Mean Invariance

In this paper, we present order invariance theoretical results for weighted quasi-arithmetic means of a monotonic series of numbers. The quasi-arithmetic mean, or Kolmogorov–Nagumo mean, generalizes the classical mean and appears in many disciplines, from information theory to physics, from economics to traffic flow. Stochastic orders are defined on weights (or equivalently, discrete probability distributions). They were introduced to study risk in economics and decision theory, and recently have found utility in Monte Carlo techniques and in image processing. We show in this paper that, if two distributions of weights are ordered under first stochastic order, then for any monotonic series of numbers their weighted quasi-arithmetic means share the same order. This means for instance that arithmetic and harmonic mean for two different distributions of weights always have to be aligned if the weights are stochastically ordered, this is, either both means increase or both decrease. We explore the invariance properties when convex (concave) functions define both the quasi-arithmetic mean and the series of numbers, we show its relationship with increasing concave order and increasing convex order, and we observe the important role played by a new defined mirror property of stochastic orders. We also give some applications to entropy and cross-entropy and present an example of multiple importance sampling Monte Carlo technique that illustrates the usefulness and transversality of our approach. Invariance theorems are useful when a system is represented by a set of quasi-arithmetic means and we want to change the distribution of weights so that all means evolve in the same direction.     

On potentially negative space time covariances obtained as sum of products of marginal ones

Most of the literature on spatio-temporal covariance models proposes structures that are positive in the whole domain. However, problems of physical, biological or medical nature need covariance models allowing for negative values or oscillations from positive to negative values. In this paper, we propose an easy-to-implement and interpretable class of models that admits this type of covariances. We show particular analytical examples that may be of interest in the biometrical context. Work partially funded by grants GV04A724 (Generalitat Valenciana) and MTM2004-06231 (Spanish Ministry of Science and Education).     

Asymmetric synthesis of fluorinated amino macrolactones through ring-closing metathesis

The synthesis of new chiral fluorinated amino and azamacrolactones of types 1 and 2 is described. A ring-closing metathesis (RCM) reaction constitutes the key step in this methodology, which uses fluorinated amino alcohols 7 as starting materials. The influence of the CF2 group, which is located in the alpha-position relative to the carbon bearing the amino group, on the efficiency of the RCM reaction is noteworthy. This method allows for the preparation of the desired fluorinated macrolactones in excellent yields.     

Capacities Associated with Calderón-Zygmund Kernels

Analytic capacity is associated with the Cauchy kernel 1/z and the L ^∞-norm. For n?∈??, one has likewise capacities related to the kernels $K_i(x)=x_i^{2n-1}/|x|^{2n}$ , 1?≤?i?≤?2, $x=(x_1,x_2)\in{\mathbb R}^2$ . The main result of this paper states that the capacities associated with the vectorial kernel (K ₁, K ₂) are comparable to analytic capacity.     

A new characterization of Sobolev spaces on $${\mathbb{R}^{n}}$$

In this paper we present a new characterization of Sobolev spaces on . Our characterizing condition is obtained via a quadratic multiscale expression which exploits the particular symmetry properties of Euclidean space. An interesting feature of our condition is that depends only on the metric of and the Lebesgue measure, so that one can define Sobolev spaces of any order of smoothness on any metric measure space.     

Interpreting Social Accounting Matrix (SAM) as an Information Channel

Information theory, and the concept of information channel, allows us to calculate the mutual information between the source (input) and the receiver (output), both represented by probability distributions over their possible states. In this paper, we use the theory behind the information channel to provide an enhanced interpretation to a Social Accounting Matrix (SAM), a square matrix whose columns and rows present the expenditure and receipt accounts of economic actors. Under our interpretation, the SAM’s coefficients, which, conceptually, can be viewed as a Markov chain, can be interpreted as an information channel, allowing us to optimize the desired level of aggregation within the SAM. In addition, the developed information measures can describe accurately the evolution of a SAM over time. Interpreting the SAM matrix as an ergodic chain could show the effect of a shock on the economy after several periods or economic cycles. Under our new framework, finding the power limit of the matrix allows one to check (and confirm) whether the matrix is well-constructed (irreducible and aperiodic), and obtain new optimization functions to balance the SAM matrix. In addition to the theory, we also provide two empirical examples that support our channel concept and help to understand the associated measures.