Combining machine learning with multimodal electrochemical sensing can significantly improve the analytical performance of biosensors, according to new findings from a Penn State research team. These improvements may benefit noninvasive health monitoring, such as testing that involves saliva or sweat. The findings were published this month in Analytica Chimica Acta.
The sale started early, but there’s still plenty of time to grab a brand-new Pixel 7 or Pixel Watch at a discount.
Limited budget? These affordable gizmos are WIRED favorites, and now they’re even cheaper.
Alexa might be going through some hardships, but you can save on Kindles, Fire tablets, and Fire TV sticks.
We’ve found discounts on bedsheets, weighted blankets, and sound machines to help make bedtime a little sweeter.
Every television deserves a row of speakers to call its own. These are our favorites.
Research finds using a large collection of simple, un-curated synthetic image generation programs to pretrain a computer vision model for image classification yields greater accuracy than employing other pretraining methods that are more costly and time consuming, and less scalable.
In recent years, deep learning techniques have proved to be highly valuable for tackling countless research and real-world problems. Researchers at Feedzai, a financial data science company based in Portugal, have demonstrated the potential of deep learning for the prevention and detection of illicit money laundering activities.
Researchers at the Electronics and Telecommunications Research Institute (ETRI) in Korea have recently developed a deep learning-based model that could help to produce engaging nonverbal social behaviors, such as hugging or shaking someone’s hand, in robots. Their model, presented in a paper pre-published on arXiv, can actively learn new context-appropriate social behaviors by observing interactions …
Read more “A deep learning model that generates nonverbal social behavior for robots”
A trio of researchers from the University of California, working with a colleague from the Institute of Computer Science of the Czech Academy of Sciences, has found that it is possible to prevent catastrophic forgetting in AI systems by having such systems mimic human REM sleep.