Physicists develop new protocol for building photonic graph states

Physicists have long recognized the value of photonic graph states in quantum information processing. However, the difficulty ...
Frontiers

Graph Learning and Deep Unfolding for Electromagnetic Field Modelling and Antenna Systems

Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...

Leading AI models struggle to solve original math problems

Mathematics, like many other scientific endeavors, is increasingly using artificial intelligence. Of course, math is the backbone of AI, but mathematicians are also turning to these tools for tasks ...
Hackaday

Hackaday Podcast Episode 356: Nanoprinting, Vibe Coding, And Keebin’ With Kristina, IN HELL!

This week, Hackaday’s Elliot Williams and Kristina Panos met up over coffee to bring you the latest news, mystery sound ...
IEEE

Spatio-Temporal-Spectral Hierarchical Graph Convolutional Network With Semisupervised Active Learning for Patient-Specific Seizure Prediction

Abstract: Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced technique for seizure prediction. Recent deep learning approaches, which fail to fully explore both ...
IEEE

Using path-finding algorithms of graph theory for route-searching in geographical information systems

Abstract: This paper deals with graph theory application in large-scale geographical data searching and visualization. We present a comparison of two path-finding algorithms of graph theory, i.e.