Summary We propose double/debiased machine learning approaches to infer a parametric component of logistic partially linear model. Our framework is based on Neyman orthogonal score equation consisting two nuisance models for the nonparametric model and conditional mean exposure with control group. To estimate models, we separately consider use high dimensional (HD) sparse regression (nonparamet...

This paper studies two important questions related with switched reluctance machines: 1. In what conditions we can consider to use the SRM linear model but with sufficient accuracy to predict its steady-state characteristics? 2. To take or not to take into account the mutual inductances in the 6/4 SR non-linear model? Are there any significant errors when we neglect the phase mutual inductances...

Introduction: The brain tumor is an abnormal growth of tissue in the brain, which is one of the most important challenges in neurology. Brain tumors have different types. Some brain tumors are benign and some brain tumors are cancerous and malignant. Glioblastoma Multiforme (GBM) is the most common and deadliest malignant brain tumor in adults. The average survival rate for peo...

In machine translation, discriminative models have almost entirely supplanted the classical noisychannel model, but are standardly trained using a method that is reliable only in low-dimensional spaces. Two strands of research have tried to adapt more scalable discriminative training methods to machine translation: the first uses log-linear probability models and either maximum likelihood or mi...

− In the last decades different topologies of special electrical machines were studied. One of the most interesting machine belonging to this category is the transverse flux machine. The authors focused their attention on the linear variant of this type of machine. Up today a small number of studies have approached the linear transverse flux machine, so the new perspective given should be of re...

By using of analytical multi-logic expresses in conjunction with non-deterministic Turing machine the proposition was proved that algorithm of deterministic Turing counter machine of polynomial time complexity can be decreased to the algorithm of linear time complexity in non-deterministic Turing counter machine. Furthermore, it was shown that existence of reduction of polynomial time complexit...

This paper defines head linear reduction, a reduction strategy of λ-terms that performs the minimal number of substitutions for reaching a head normal form. The definition relies on an extended notion of redex, and head linear reduction is therefore not a strategy in the exact usual sense. Krivine’s Abstract Machine is proved to be sound by relating it both to head linear reduction and to usual...