We use multiscale-multispace correlations and Fourier transform techniques, to study some intermittent random field properties, which escape analysis by structure function scaling. These properties are parametrized in terms of a set of scale ratios, giving the typical interaction distances in space and scale of the random field fluctuations, and the characteristic lengths over which these fluct...

Modern geostatistical mapping methods are being applied to various types of data to produce more realistic and flexible characterizations of a natural random process. The Bayesian Maximum Entropy (BME) is a well-known geostatistical estimation method, especially for the use of soft knowledge as well as exact measurement data. Although development in geostatistical methods helps us to solve limi...

-A new hybrid method is presented that combines the scale space filter (SSF) and Markov random field (MRF) for color image segmentation. The fundamental idea of the SSF is to use the convolution of Gaussian functions and image-histogram to generate a scale space image and then find the proper interval bounded by the local extrema of the derivatives. The Gaussian function is with zero mean and v...

We establish mean-field limits for large-scale random-access networks with buffer dynamics and arbitrary interference graphs. Although saturated scenarios have been widely investigated yield useful throughput estimates persistent sessions, they fail to capture the fluctuations in contents over time provide no insight delay performance of flows intermittent packet arrivals. Motivated by that iss...

We consider the problem of magnetic-energy amplification by a turbulent flow of conducting fluid. This effect is known as the small-scale turbulent dynamo, and it is believed to be relevant in application to magnetic fields in astrophysical objects. It is, in fact, a generic property of random (in time and/or space) flows that they can amplify magnetic fluctuations at scales smaller than the sc...

When used for structured regression, powerful Conditional Random Fields (CRFs) are typically restricted to modeling effects of interactions among examples in local neighborhoods. Using more expressive representation would result in dense graphs, making these methods impractical for large-scale applications. To address this issue, we propose an effective CRF model with linear scale-up properties...

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...