It has been shown that the single-point probability density functions of the data of certain cloud properties, such as cloud optical depth, are positively skewed and resemble the probability density functions of a log-normal random variable (e.g. Barker et al., 1996). There is also evidence that, for certain spatial scales, the two-point statistics are such that the energy spectrum Ek satisfies...

We consider the problem of high-dimensional structured estimation with norm-regularized estimators, such as Lasso, when the design matrix and noise are drawn from sub-exponential distributions. Existing results only consider sub-Gaussian designs and noise, and both the sample complexity and non-asymptotic estimation error have been shown to depend on the Gaussian width of suitable sets. In cont...

State of the art language recognition systems usually add a backend prior to the linear fusion of the subsystems scores. The backend plays a dual role. When the set of languages for which models have been trained does not match the set of target languages, the backend maps the available scores to the space of target languages. On the other hand, the backend serves as a precalibration stage that...

Let X be a norm curve in the SL(2,C)-character variety of a knot exterior M . Let t = ‖β‖/‖α‖ be the ratio of the Culler-Shalen norms of two distinct non-zero classes α, β ∈ H1(∂M,Z). We demonstrate that either X has exactly two associated strict boundary slopes ±t, or else there are strict boundary slopes r1 and r2 with |r1| > t and |r2| < t. As a consequence, we show that there are strict bou...

The L1 norm has been applied in numerous variations of principal component analysis (PCA). L1-norm PCA is an attractive alternative to traditional L2-based PCA because it can impart robustness in the presence of outliers and is indicated for models where standard Gaussian assumptions about the noise may not apply. Of all the previously-proposed PCA schemes that recast PCA as an optimization pro...

The goal of this paper is to give a functional relationship between the membership functions of fuzzy intervals M1⊕...⊕Mn and M1 ... Mn, where Mi are positive LR fuzzy intervals of the same form Mi = M = (a, b, α, β)LR and the extended addition ⊕ and multiplication are defined in the sense of a triangular norm (i.e. via sup–t–norm convolution).

Let ‖ · ‖ be the euclidean norm on R and γn the (standard) Gaussian measure on R with density (2π)e 2/2. Let θ (≃ 1.3489795) be defined by γ1([−θ/2, θ/2]) = 1/2 and let L be a lattice in R n generated by vectors of norm ≤ θ. Then, for any closed convex set V in R with γn(V ) ≥ 1 2 and for any a ∈ R, (a + L) ∩ V 6= φ. The above statement can be viewed as a “nonsymmetric” version of Minkowski The...

It has been speculated that the human motion system combines noisy measurements with prior expectations in an optimal, or rational, manner. The basic goal of our work is to discover experimentally which prior distribution is used. More specifically, we seek to infer the functional form of the motion prior from the performance of human subjects on motion estimation tasks. We restricted ourselves...

and Applied Analysis 3 nonlinear third-order differential equations. Section 5 gives the corresponding results for those obtained in Sections 2, 3, and 4, but for the fifth-order differential equations. In Section 6, we present some numerical results exhibiting the accuracy and efficiency of our numerical algorithms. 2. SJG Method for Third-Order Differential Equations with Constant Coefficient...

E-mail: [email protected] Abstract: We determine the exact order of the p-average linear n-widths λ n (W r 2 , μ, Lq)p, 1 ≤ q < ∞, 0 < p <∞, of the Sobolev space W r 2 equipped with a Gaussian measure μ in the Lq-norm. Moreover, we also calculate the probabilistic linear (n, δ)-widths and p-average linear n-widths of the finitedimensional space R with the standard Gaussian measure in l q , i.e.,