Quantification of soil aggregation and erodibility from easily measurable characteristics have been done by using pedo-transfer functions (PTFs) PTFs developed were compared statistical machine learning techniques for the kandi region Punjab. Dataset 1, having six basic properties, was used estimation mean weight diameter (MWD) (K), prediction an artificial neural network (ANN) slightly better ...

In this paper, we try to solve the problem of temporal link prediction in information networks. This implies predicting the time it takes for a link to appear in the future, given its features that have been extracted at the current network snapshot. To this end, we introduce a probabilistic nonparametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden...

By using themethods of linear algebra andmatrix inequality theory, we obtain the characterization of admissible estimators in the general multivariate linear model with respect to inequality restricted parameter set. In the classes of homogeneous and general linear estimators, the necessary and suffcient conditions that the estimators of regression coeffcient function are admissible are establi...

A Generalized Langevin Model (GLM) formulation to be used in transported joint velocity-scalar probability density function methods is recalled order imply a turbulent scalar-flux model where the pressure-scrambling term correspondence with standard Monin's return-to-isotropy term. The proposed non-constant C0 extended seen-velocity models for particle dispersion modeling dispersed two-phase fl...

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying ...

Introduction: Image registration of fMRI data only corrects for bulk movements while leaving secondary artifacts such as, spin history effects, motion induced dynamic field inhomogeneity changes, and interpolation errors untouched. Secondary artifacts increase variability in time-series and reduce sensitivity of activation detection. Methods such as the use estimated motion parameters as “Nuisa...

AIM This study compared PR and NB in predicting HCV patient costs. The objective of this study was to predict the direct cost of the HCV patient in Iran. BACKGROUND Hepatitis C virus (HCV) is a common and expensive infectious disease in Iran. Cost associated with HCV and its complications has not been well characterized. Analysis of cost data is important in providing consistent information t...

With Fq a finite field of characteristic p, let F(q) be the category whose objects are functors from finite dimensional Fq–vector spaces to F̄p– vector spaces. Extension groups in F(q) can be interpreted as MacLane (or Topological Hochschild) cohomology with twisted coefficients. Furthermore, evaluation on an m dimensional vector space Vm induces a homorphism from Ext F(q) (F, G) to finite group...

Stimulus reconstruction or decoding methods provide an important tool for understanding how sensory and motor information is represented in neural activity. We discuss Bayesian decoding methods based on an encoding generalized linear model (GLM) that accurately describes how stimuli are transformed into the spike trains of a group of neurons. The form of the GLM likelihood ensures that the post...