Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs ...

Nonlinear mixed effects models (NLMMs) and self-modeling nonlinear regression (SEMOR) models are often used to fit repeated measures data. They use a common function shared by all subjects to model ...

UCLA researchers demonstrate diffractive optical processors as universal nonlinear function approximators using linear ...

Materials scientists can learn a lot about a sample material by shooting lasers at it. With nonlinear optical microscopy—a specialized imaging technique that looks for a change in the color of intense ...

Given appropriate models for vacuum tubes, circuit simulators such as SPICE make analysis based on such nonlinear models a tractable effort. The Langmuir, Leach, and Koren models are discussed for the ...

A standard digital camera used in a car for stuff like emergency braking has a perceptual latency of a hair above 20 milliseconds. That’s just the time needed for a camera to transform the photons ...

Author Dr. R. Russell Rhinehart discusses his new book, Nonlinear Model-Based Control: Using First-Principles Models in Process Control, and explains why nonlinear first-principles models should be ...

We leverage recent advancements in machine learning to develop an integrated method to solve globally and estimate models featuring agent heterogeneity, nonlinear constraints, and aggregate ...

This is a preview. Log in through your library . Abstract Multinomial logit models, especially those calibrated on scanner data, often use explanatory variables that are nonlinear functions of the ...

Activation functions play a critical role in AI inference, helping to ferret out nonlinear behaviors in AI models. This makes them an integral part of any neural network, but nonlinear functions can ...