The absence of reliable data on fundamental economic indicators (e.g. real GDP), combined with structural shifts in the economy, can severely constrain the ability to conduct accurate macroeconomic ...

Artificial intelligence is reshaping cybersecurity, but much of that progress has focused on cloud and enterprise ...

Data-driven AI systems increasingly influence our choices, raising concerns about autonomy, fairness, and accountability. Achieving algorithmic autonomy requires new infrastructures, motivation ...

As AI workloads move from centralized cloud infrastructure to distributed edge devices, design priorities have fundamentally ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

AI in pharma has the potential to overhaul drug discovery, clinical trials, manufacturing, and marketing by analysing vast datasets to speed up processes, reduce costs, and enable personalised ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

How do you translate ancient Palmyrene script from a Roman tombstone? How many paired tendons are supported by a specific ...

Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving classification and segmentation tasks.

Optical AI is revolutionizing sensor technology, integrating sensing and processing for efficient, low-power intelligent vision in real-time applications.

Scientists at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory have developed a novel artificial ...

Achieving high reliability in AI systems—such as autonomous vehicles that stay on course even in snowstorms or medical AI that can diagnose cancer from low-resolution images—depends heavily on model ...