When children learn their native language, they have to deal with a confusing array of dependencies between various elements in an utterance. The dependent elements may be adjacent to one another or separated by intervening material. Prior studies suggest that nonadjacent dependencies are hard to learn when the intervening material has little variability, which may be due to a tradeoff between ...

A method is proposed to transform the red and near infrared data into a soil index in order to map soil features. The index maximises soil variation and helps improve soil feature mapping while it suppresses spectral response from vegetation cover. Geo-statistical analysis of fi eld data, on the other hand, helps understand spatial dependency pattern and map it, which may not be directly visibl...

In this letter, the problem of sparse signal reconstruction from one bit compressed sensing measurements is investigated. To solve the problem, a variational Bayes framework with a new statistical multivariate model is used. The dependency of the wavelet decomposition coefficients is modeled with a multivariate Gaussian copula. This model can separate marginal structure of coefficients from the...

We investigated the developmental trajectory of nonadjacent dependency learning in an artificial language. Infants were exposed to 1 of 2 artificial languages with utterances of the form [aXc or bXd] (Grammar 1) or [aXd or bXc] (Grammar 2). In both languages, the grammaticality of an utterance depended on the relation between the 1st and 3rd elements, whereas the intervening element varied free...

In this paper we propose two interpretations for the discrimination parameter in the two-parameter logistic model (2PLM). The interpretations are based on the relation between the 2PLM and two stochastic models. In the first interpretation, the 2PLM is linked to a diffusion model so that the probability of absorption equals the 2PLM. The discrimination parameter is the distance between the two ...

The spatial inequality of the environment arises as a planning problem when the spatial structure of the different districts of a city is distinctly different; Differences that require different programming solutions for different regions and meeting the objective of creating spatial equality in a city. Nobahar and Vaki-Agha are two districts with unequal urban space located in north and south ...

We present a novel, semi-supervised approach to training discriminative random fields (DRFs) that efficiently exploits labeled and unlabeled training data to achieve improved accuracy in a variety of image processing tasks. We formulate DRF training as a form of MAP estimation that combines conditional loglikelihood on labeled data, given a data-dependent prior, with a conditional entropy regul...

This study examined to study how respondents rely on the Internet to fulfill the various life goals dimensioned into understanding, orientation and playing goals, and how this dependency relates to the generation of social capital. Further, it examined the resources of social capital in terms of bonding social capita and bridging social capital. In this study quantitative research approach was ...

This article examines the spatial variability in the distribution of crime in the City of Savannah, GA, where a total of 12,458 crimes were reported in 2000. All crimes were geocoded and the same numbers of control locations were generated under the assumption that the spatial distribution of crime is a realization of an inhomogeneous Poisson process (spatially random). The control locations se...

In dictionary learning for analysis of images, spatial correlation from extracted patches can be leveraged to improve characterization power. We propose a Bayesian framework for dictionary learning, where spatial location dependencies are captured by imposing a multiplicative Gaussian process prior on the latent units representing binary activations. Data augmentation and Kronecker methods allo...