Feature model validation aims to identify errors in feature models. The two major errors, called dead features and false variable features, are caused by contradictory feature relationships in a feature model. Current existing approaches use constraint satisfaction problem (CSP) and CSP solvers to identify these feature model errors. However, CSP is a NPcomplete problem and CSP solvers reveal a...

A prospective study was performed comparing the fluorescent antibody to membrane antigen (FAMA) test and the enzyme-linked immunosorbent assay (ELISA) for identifying susceptibility and seroconversion to varicella-zoster virus (VZV) infection. A total of 75 sera were collected from index cases and from sibling and parent contacts in 10 families. Varicella-zoster virus-infected human diploid emb...

capital asset pricing, as one of the basic theories in finance and investment area, develop a model for estimation of expected rate of return and equity cost of capital. this model has many applications in the field of finance. one of anomalies in the capital asset pricing model is the value premium that its proponents believe this risk premium is compensation for a risk not mentioned in origin...

This paper establishes an econometric framework to construct a systematic risk factor from textual data, by linking a beta pricing model with a language model based on machine learning techniques. In this framework, the distributions of stock returns and words in associated documents are determined by a common underlying systematic risk factor (text-implied risk). The exposure to the text-impli...

Fluid antenna multiple access (FAMA) is a new way of accommodating large number users on single channel for massive connectivity, with slow FAMA (s-FAMA) being the practical version achieving this. The impressive performance understood to be achievable if have independent Rayleigh fading envelopes. With mobile networks vamping up operating frequencies in 5G and beyond, nevertheless, will less m...

Abstract We propose a dynamic factor state-space model for the prediction of high-dimensional realized covariance matrices asset returns. Using block LDL decomposition joint matrix assets and factors, we express individual similar to an approximate model. parts, i.e., residual covariances as well loadings, independently via tractable approach. This results in closed-form Matrix- F predictive de...