Federated Learning (FL) allows for privacy-preserving model training by enabling clients to upload model gradients without exposing their personal data. However, the decentralized nature of FL ...

The development of quantum processors for practical fluid flow problems is a promising yet distant goal. Recent advances in quantum linear solvers have highlighted their potential for classical fluid ...

In a paper published January 21 in Nature, a team led by scientists at the University of Wisconsin–Madison have run complex numerical simulations of plasma flows that, while leading to turbulence, ...

Abstract: As technology scales to smaller nanometer nodes, Electromigration (EM) has become one of the most significant challenges in the EDA industry. Due to the reduction of the interconnect ...