Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
and Johnson, Charles R
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
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Quantum algorithm block-encodes Riccati solutions for m-particle m-hole RPA using Riesz projectors and QSVT, claiming linear system-size scaling under sparsity and polynomial cost in excitation rank m.
Erlang mixture approximations with the linear chain trick convert distributed delay DDEs into ODEs, with convergence proofs for bounded kernels and applications to stability analysis.
Two generalizations of reduced rank extrapolation are derived for low-rank matrix sequences and iteration-dependent mapping functions, with numerical tests on Lyapunov and Riccati equations.
Generalizes Williamson's theorem to real symmetric matrices allowing arbitrary real symplectic eigenvalues, with explicit constructions and perturbation bounds for the class EigSpSm(2n).
Stimulus symmetries render many neural representations functionally equivalent yet produce qualitatively different RSMs, including drifting ones from SGD or regularization in image-encoding networks.
A CTM-GNN model with EnSRF assimilation and flow-weighted transition matrix fuses floating car data and camera observations to deliver physically consistent, network-wide traffic volume estimates and forecasts, demonstrated with improved accuracy in Manhattan.
Classifies faces of copositive and completely positive cones over the second-order cone, examines dimension and exposedness, and computes two chain-related parameters.