arXiv subjects

Woosung Song

Publications and source records attributed to Woosung Song.

2 recordsLinked to original sources

arXiv2025

Quantum Suppression of Mass Inflation in Reissner-Nordstr\"{o}m Interiors via Wheeler-DeWitt Equation

We construct a canonical quantization, the Wheeler-DeWitt equation, of the interior geometry of static and spherically symmetric black holes in Einstein-Maxwell-$\Lambda$ framework, focusing on Reissner-Nordstr\"{o}m. The wave function of the Wheeler-DeWitt equation for the Reissner-Nordstr\"{o}m black hole is set to be on-shell and exhibiting exponential damping away from the classical locus. Horizon boundary conditions for the wave function generate two regimes: a single inward mode from event horizon yields monotonic decay, while superpositions produce either a quantum bounce (single time arrow) or interference-driven annihilation-to-nothing (two time arrows). We show that these are generic features of static black hole interiors. Furthermore, the wave function of the Schwarzschild black hole, obtained as the charge-neutral limit of the Reissner-Nordstr\"{o}m black hole, is monotonically decaying and no longer unbounded. Moreover, this framework unifies classical and quantum interiors, suggests a quantum gravitational suppression to the mass inflation, and motivates extensions to Kerr and regular black holes.

gr-qc↗

arXiv2021

A Static Analyzer for Detecting Tensor Shape Errors in Deep Neural Network Training Code

We present an automatic static analyzer PyTea that detects tensor-shape errors in PyTorch code. The tensor-shape error is critical in the deep neural net code; much of the training cost and intermediate results are to be lost once a tensor shape mismatch occurs in the midst of the training phase. Given the input PyTorch source, PyTea statically traces every possible execution path, collects tensor shape constraints required by the tensor operation sequence of the path, and decides if the constraints are unsatisfiable (hence a shape error can occur). PyTea's scalability and precision hinges on the characteristics of real-world PyTorch applications: the number of execution paths after PyTea's conservative pruning rarely explodes and loops are simple enough to be circumscribed by our symbolic abstraction. We tested PyTea against the projects in the official PyTorch repository and some tensor-error code questioned in the StackOverflow. PyTea successfully detects tensor shape errors in these codes, each within a few seconds.

cs.LG↗