Sequence kernel association test (SKAT) has become one of the most commonly used nonburden tests for analyzing rare variants. Performance of burden tests depends on the weighting of rare and common variants when collapsing them in a genomic region. Using the systolic and diastolic blood pressure phenotypes of 142 unrelated individuals in the Genetic Analysis Workshop 18 data, we investigated wh...

In this paper, we investigate the impact of several local and global weighting schemes on Latent Semantic Analysis’ (LSA) ability to capture semantic similarity between two texts. We worked with texts varying in size from sentences to paragraphs. We present a comparison of 3 local and 3 global weighting schemes across 3 different standardized data sets related to semantic similarity tasks. For ...

The article considers the issue of reducing the influence of clayless mud components on the filtration parameters of the reservoir. In the composition of such solutions, calcium carbonate of various fractions is often added as a weighting and colmatizing additive. It is generally accepted that weighting bridging agents are sufficiently inert substances and their addition does not affect the rhe...

In this paper the optimisation of the weighting functions for an H∞ controller using genetic algorithms and structured genetic algorithms is considered. The choice of the weighting functions is one of the key steps in the design of an H∞ controller. The performance of the controller depends on these weighting functions since poorly chosen weighting functions will provide a poor controller. One ...

k-nearest neighbor (kNN) is a widely used learning algorithm for supervised tasks. In practice, the main challenge when using kNN its high sensitivity to hyperparameter setting, including number of nearest neighbors k, distance function, and weighting function. To improve robustness hyperparameters, this study presents novel method based on graph neural network, named kNNGNN. Given training dat...

This paper investigates a method of reconstructing images from energy-resolved CT data with negligible beam-hardening artifacts and improved contrast-to-nosie ratio (CNR) compared to conventional energy-weighting methods. Conceptually, the investigated method first reconstructs separate images from each energy bin. The final image is a linear combination of the energy-bin images, with the weigh...