This paper compares a family of methods for characterizing neural feature selectivity with natural stimuli in the framework of the linear-nonlinear model. In this model, the neural firing rate is a nonlinear function of a small number of relevant stimulus components. The relevant stimulus dimensions can be found by maximizing one of the family of objective functions, Rényi divergences of differ...

Determination of the rate of energy loss in a reverberation chamber is fundamental to many different measurements such as absorption cross-section, antenna efficiency, radiated power, and shielding effectiveness. Determination of the energy decay time-constant in the time-domain by linear fitting the power delay profile, rather than using the frequencydomain quality-factor, has the advantage of...

We perform N-body simulations of theories with infinite-volume extra dimensions, such as the DvaliGabadadze-Porrati model and its higher-dimensional generalizations, where 4D gravity is mediated by massive gravitons. The longitudinal mode of these gravitons mediates an extra scalar force, which we model as a density-dependent modification to the Poisson equation. This enhances gravitational clu...

This contribution gives a brief overview on nonlinear constrained least squares methods. The focus is on data fitting problems in which the models include ordinary or partial differential equations.

Google Earth Engine (GEE) has been widely used to process geospatial data in recent years. Although the current GEE platform includes functions for fitting linear regression models, it does not have function fit nonlinear limiting platform’s capacity and application. To circumvent this limitation, work proposes a general adaptation of Levenberg–Marquardt (LM) method models parallel processing f...