The height function of TASEP with space-time discontinuous speed function converges to a deterministic limit given by a Lax-Oleinik variational formula that satisfies a discontinuous Hamilton-Jacobi equation.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 2polarities
background 2representative citing papers
Schur-complement reduction of CRNs is the categorical complement of the stoichiometric arrow in [A₂, Vect], with a reconstruction functor and adjunction to the stoichiometric functor.
Sharp convergence rates and concentration bounds are established for empirical measures of point processes under a newly introduced Wasserstein distance on counting measures.
Simulations show Ridge, Lasso, and ElasticNet perform similarly for prediction at high sample-to-feature ratios, but Lasso feature selection recall drops to 0.18 under high multicollinearity and low SNR while ElasticNet holds at 0.93.
citing papers explorer
-
Hydrodynamic limits for TASEP with space-time discontinuities
The height function of TASEP with space-time discontinuous speed function converges to a deterministic limit given by a Lax-Oleinik variational formula that satisfies a discontinuous Hamilton-Jacobi equation.
-
Categorical Perspectives on Chemical Reaction Networks
Schur-complement reduction of CRNs is the categorical complement of the stoichiometric arrow in [A₂, Vect], with a reconstruction functor and adjunction to the stoichiometric functor.
-
Wasserstein convergence rates for empirical measures of point processes
Sharp convergence rates and concentration bounds are established for empirical measures of point processes under a newly introduced Wasserstein distance on counting measures.
-
Choosing the Right Regularizer for Applied ML: Simulation Benchmarks of Popular Scikit-learn Regularization Frameworks
Simulations show Ridge, Lasso, and ElasticNet perform similarly for prediction at high sample-to-feature ratios, but Lasso feature selection recall drops to 0.18 under high multicollinearity and low SNR while ElasticNet holds at 0.93.