Imbalanced classification refers to problems in which there are significantly more instances available for some classes than others. Such scenarios require special attention because traditional classifiers tend be biased towards the majority class has a large number of examples. Different strategies, such as re-sampling, have been suggested improve imbalanced learning. Ensemble methods also pro...

The concept of disjunct eddy sampling (DES) for use in measuring ecosystem-level micrometeorological fluxes is re-examined. The governing equations are discussed as well as other practical considerations and guidelines concerning this sampling method as it is applied to either the disjunct eddy covariance (DEC) or disjunct eddy accumulation (DEA) techniques. A disjunct eddy sampling system was ...

Background Dramatic increases in rna structural data have made it possible to recognize its conformational preferences much better than a decade ago. This has created an opportunity to use discrete restraint-based conformational sampling for modelling rna and automating its crystallographic re nement. Results All-atom sampling of entire rna chains, termini and loops is achieved using the Richar...

This paper performs systematic comparative studies on rough set based class imbalance learning. We compare the strategies of weighting, re-sampling and filtering used in the rough set based methods for class imbalance learning. Weighting is better than re-sampling, and re-sampling is better than filtering. The weighted rough set based method achieves the best performance in class imbalance lear...

In current HMM/DNN speech recognition systems, the purpose of the DNN component is to estimate the posterior probabilities of tied triphone states. In most cases the distribution of these states is uneven, meaning that we have a markedly different number of training samples for the various states. This imbalance of the training data is a source of suboptimality for most machine learning algorit...

This paper describes a novel theoretical approach for the improvement of the bivariate linear interpolation function. The fundamental premise of this theory consists of quantifying the effect of the interpolation function on an image’s pixel by the product of the value of the pixel intensity times the sum of non-null second order derivatives of the function. The product is called intensity-curv...

Many crustaceans detect odors from distant sources (such as conspecifics or prey items) by using chemosensory sensillae (aesthetascs) on their antennules. The morphology and arrangement of the aesthetascs on the antennule and the movement of the antennule through the surrounding fluid during olfactory sampling affect the flow of odorants around the sensillae and thus odorant access to receptors...

Introduction: General formulations for parallel MRI using arbitrary k-space trajectories are mainly based on iterative algorithms, such as CG-SENSE [1]. In addition, an iterative GRAPPA approach for arbitrary k-space sampling has been presented [2]. However, the reconstruction process includes several steps, including gridding, GRAPPA convolution and re-sampling operations during each iteration...