Background The relationship of social determinants of health (SDOH), environmental exposures and medical history to lung function trajectories is underexplored. A better understanding of these ...

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...

A new technical paper titled “Massively parallel and universal approximation of nonlinear functions using diffractive processors” was published by researchers at UCLA. “Nonlinear computation is ...

Abstract: This letter proposes a novel complex-valued nonlinear function (CVNF) approximation method for adaptive nonlinear filter, which is used to digitally cancel the weak nonlinearity, a commonly ...

Abstract: We propose and experimentally demonstrate a reconfigurable nonlinear activation function (NAF) unit based on add-drop resonator Mach-Zehnder interferometers (ADRMZIs) for photonic neural ...

"If you want to just be a lawyer, fine. But I think it's an excellent use of your talent to expand beyond legal if you're interested," said Laura Schumacher, former chief legal officer of AbbVie.

Penn Engineers have created the first programmable photonic chip that can train nonlinear neural networks using light, potentially revolutionizing AI by making it faster and more energy-efficient.

Postdoctoral fellow Tianwei Wu (left) and Professor Liang Feng (right) in the lab, demonstrating some of the apparatus used to develop the new, light-powered chip. Penn Engineers have developed 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 ...

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 ...