Frontiers

Leveraging linear mapping for model-agnostic adversarial defense

1 Department of Computer Science, Colorado State University, Fort Collins, CO, United States 2 Department of Mathematics, Colorado State University, Fort Collins, CO, United States In the ...
Nature

Generalized linear mixed models for mapping multiple quantitative trait loci

Linear mixed model (LMM) methodology is a powerful technology to analyze models containing both the fixed and random effects. The model was first proposed to estimate genetic parameters for unbalanced ...
Nature

Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits

Given the importance of cereal grain seeds as the staple food and nutrition resources for humans and animals, and raw materials for food industry, understanding the genetic architecture underlying the ...
IEEE

Single-Image Super-Resolution via Linear Mapping of Interpolated Self-Examples

Abstract: This paper presents a novel example-based single-image superresolution procedure that upscales to high-resolution (HR) a given low-resolution (LR) input image without relying on an external ...
IEEE

A linear inverse space-mapping (LISM) algorithm to design linear and nonlinear RF and microwave circuits

Abstract: A linear inverse space-mapping (LISM) optimization algorithm for designing linear and nonlinear RF and microwave circuits is described in this paper. LISM is directly applicable to microwave ...
Frontiers

Quantifying the Brain Predictivity of Artificial Neural Networks With Nonlinear Response Mapping

Quantifying the similarity between artificial neural networks (ANNs) and their biological counterparts is an important step toward building more brain-like artificial intelligence systems. Recent ...