The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential
to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends …

Mechanical metamaterials are meticulously designed structures with exceptional
mechanical properties determined by their microstructures and constituent materials …

Deep-learning models have become pervasive tools in science and engineering. However,
their energy requirements now increasingly limit their scalability. Deep-learning …

Aside from some living tissues, few materials can autonomously learn to exhibit desired
behaviors as a consequence of prolonged exposure to unanticipated ambient loading …

Learning is traditionally studied in biological or computational systems. The power of
learning frameworks in solving hard inverse problems provides an appealing case for the …

This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

Equilibrium propagation (EP) is an alternative to backpropagation (BP) that allows the
training of deep neural networks with local learning rules. It thus provides a compelling …

Energy-based learning algorithms have recently gained a surge of interest due to their
compatibility with analog (post-digital) hardware. Existing algorithms include contrastive …

Fluorescent carbon dots (CDs) have been increasingly used in fluorescence detection and
imaging based on their tunable fluorescence (FL) and resistance to photobleaching …

In typical artificial neural networks, neurons adjust according to global calculations of a
central processor, but in the brain, neurons and synapses self-adjust based on local …