Recently, deep learning-based image denoising methods have achieved promising performance. However, when the distribution of real-world noisy images is unknown, performance still limited due to domain gap between training set and testing set. Nonetheless, unknown noise usually can be modeled as proper combination existing distributions. In this paper, we propose a simple yet effective Bayesian ...

Ensemble methods are a reliable way to combine several models achieve superior performance. However, research on the application of ensemble in remote sensing object detection scenario is mostly overlooked. Two problems arise. First, one unique characteristic Oriented Bounding Boxes (OBB) objects and fusion multiple OBBs requires further attention. Second, widely used deep learning detectors pr...

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically Internet and social media), human communications have reformed notably from face to communication. Generally, Automatic Personality Prediction (or Perception) (APP) automated forecasting on different types gener...

A molecular simulation method to study the dynamics of chemically reacting mixtures is presented. The method uses a combination of stochastic and dynamic simulation steps, allowing for the simulation of both thermodynamic and transport properties. The method couples a molecular dynamics simulation cell (termed dynamic cell) to a reaction mixture simulation cell (termed control cell) that is for...

In this paper we present the theoretical background for destructive component identification in ensemble method via multivariate decompositions. The identification method is based on second order statistics and it is addressed for predictive models scored by MSE criterion. The validity of the concept is confirmed by simulation study based on Friedman benchmark function. Key-Words: Ensemble meth...