Designed Radiopharmaceuticals: How Machine Learning Is Redefining Precision Cancer Therapy" reports on the integration of deep learning and generative AI in radiopharmaceutical medicine, its impact on ...

From smart-baby monitors to stuffed animals embedded with LLMs, kids and parents are increasingly bombarded with AI ...

Brandee Gruener is a digital editor and writer with 20 years of experience. Her articles on gardening, homes, food, and health have appeared in Hunker, American Gardener, and other national and ...

Deep learning is a subset of machine learning that uses multi-layer neural networks to find patterns in complex, unstructured data like images, text, and audio. What sets deep learning apart is its ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Abstract: The non-convexity of rate-splitting precoder design precludes the direct use of efficient convex optimization algorithms. Instead, successive convex approximation (SCA)-based methods have ...

Enhancing enzyme catalytic efficiency and thermal stability is crucial for biocatalytic and industrial applications. While structure-based protein sequence design can improve thermal stability, its ...

An important but unresolved question in deep learning for EEG decoding is which features neural networks learn to solve the task. Prior interpretability studies have mainly explained individual ...

Max Delbrück Center for Molecular Medicine in the Helmholtz Association Altuna Akalin and his team at the Max Delbrück Center have developed a new tool to more precisely guide cancer treatment.

Abstract: This letter proposes a deep learning-based inverse design framework for two-port electromagnetic(EM) structures, which synergistically integrates deep residual neural networks (ResNet) with ...