Inverse problems in statistical physics are motivated by the challenges of ‘big data’ in different fields, in particular high-throughput experiments in biology. In inverse problems, the usual procedure of statistical physics needs to be reversed: Instead of calculating observables on the basis of model parameters, we seek to infer parameters of a model based on observations. In this review, we ...

background knowledge of radiology staff in educational hospital can play a major role in improving quality of radiological images and level of medical diagnostic. thus, the purpose of this study, evaluation skill of radiographers is specially radiology sciences. material and methods this study is descriptive and cross-sectional. a questionnaire according to course topics was prepared including ...

A research topic that has been receiving much interest recently is the reconstruction of a high resolution image from an ensemble of low resolution images of the same scene. Such image fusion implies that each low resolution image individually does not contain all of the available information, and therefore must be subject to aliasing. The reconstruction process involves resampling the ensemble...

We propose a nonparametric approach for estimating single-index, binarychoice models when parametric models such as Probit and Logit are potentially misspecified. The new approach involves two steps: first, we estimate index coefficients using sliced inverse regression without specifying a parametric probability function a priori; second, we estimate the unknown probability function using kerne...

In this paper, the effect of grey level quantization on the ability of co-occurrence probability statistics to classify natural textures is studied. Generally, as a function of increasing grey levels, many of the statistics demonstrate a decrease in classification ability while a few maintain constant classification accuracy. None of the individual statistics show increasing classification accu...

We introduce and demonstrate a bootstrap method for construction of an inverse function for the robot kinematic mapping using only sample configuration– space/workspace data. Unsupervised learning (clustering) techniques are used on pre–image neighborhoods in order to learn to partition the configuration space into subsets over which the kinematic mapping is invertible. Supervised learning is t...

A Bayesian inference approach is presented for the solution of the inverse heat conduction problem. The posterior probability density function (PPDF) of the boundary heat flux is computed given temperature measurements within a conducting solid. Uncertainty in temperature measurements is modeled as stationary zero-mean white noise. The inverse solution is obtained by computing the expectation o...

Strong, long-lived vortices play a prominent role in two-dimensional (2D) turbulent flows, and understanding their statistics is key to unravelling 2D turbulence. Here it shown that clustered same-sign forced turbulence have scale-invariant distribution of areas are described by mean-field theory. Larger clusters come apart more quickly, the flow surrounding remaining becomes progressively rand...