Intrinsic neural attractors and extrinsic environmental inputs jointly steer the dynamic trajectories of brain activity ...
New research suggests that the electrical complexity of the brain diminishes in early Alzheimer’s disease, potentially ...
Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known ...
A new artificial intelligence approach combines deep learning with physical modeling to extract detailed aerosol properties from complex satellite observations. By learning how light intensity and ...
Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...
The agent acquires a vocabulary of neuro-symbolic concepts for objects, relations, and actions, represented through a ...
Every medication in your cabinet, every material in your phone's battery, and virtually every compound that makes modern life work started as a molecular guess, with scientists hypothesizing that a ...
In a Nature Communications study, researchers from China have developed an error-aware probabilistic update (EaPU) method ...
Abstract: This work proposes a physics-constrained neural network (PCNN) method for automated and physically consistent compact modeling of semiconductor devices. Traditional ANN-based approaches ...
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