Training of physical neural networks.

Physical neural networks (PNNs) are a class of neural-like networks that make use of analogue physical systems to perform computations. Although at present confined to small-scale laboratory demonstrations, PNNs could one day transform how artificial intelligence (AI) calculations are performed. Could we train AI models many orders of magnitude larger than present ones? Could we perform model inference locally and privately on edge devices? Research over the past few years has shown that the answer to these questions is probably "yes, with enough research".

The level of acute alcohol exposure during binge drinking associates with the extent of cardiac response.

Introduction: The cardiovascular effects of acute alcohol exposure remain incompletely understood, despite its reported association with arrhythmias like atrial fibrillation (AF). The Munich-BREW II study supported a link between excessive alcohol consumption, elevated heart rate, impaired heart rate variability (HRV), and increased arrhythmia incidence. Here, we present sub-analyses exploring how the amount of congested alcohol during binge drinking and the maximum breath alcohol concentration (BAC) influence these findings.

Principles and metrics of extreme learning machines using a highly nonlinear fiber.

Optical computing offers potential for ultra high-speed and low-latency computation by leveraging the intrinsic properties of light, such as parallelism and linear as well as nonlinear ultra-high bandwidth signal transformations. Here, we explore the use of highly nonlinear optical fibers (HNLFs) as platforms for optical computing based on the concept of extreme learning machines (ELMs). To evaluate the information processing potential of the system, we consider both task-independent and task-dependent performance metrics.