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Markov Decision Processing Networks
Sanidhay Bhambay, Thirupathaiah Vasantam, Neil Walton
https://arxiv.org/abs/2509.24541 https://arxiv.org/pdf/2509.24541…

Markov Decision Processing Networks
We introduce Markov Decision Processing Networks (MDPNs) as a multiclass queueing network model where service is a controlled, finite-state Markov process. The model exhibits a decision-dependent service process where actions taken influence future service availability. Viewed as a two-sided queueing model, this captures settings such as assemble-to-order systems, ride-hailing platforms, cross-skilled call centers, and quantum switches. We first characterize the capacity region of MDPNs. Unli…

High-Precision Scale Setting with the Omega-Baryon Mass and Gradient Flow
Alexei Bazavov, Claude W. Bernard, David A. Clarke, Carleton DeTar, Aida X. El-Khadra, Elvira G\'amiz, Steven Gottlieb, Anthony V. Grebe, Urs M. Heller, Leon Hostetler, William I. Jay, Hwancheol Jeong, Andreas S. Kronfeld, Yin Lin, Shaun Lahert, Jack Laiho, Michael Lynch, Andrew T. Lytle, Aaron S. Meyer, Ethan T. Neil, Curtis T. Peterson, James N. Simone, Jacob W. Sitison, Ruth S. Van de Water, Alejandro Vaqu…

High-Precision Scale Setting with the Omega-Baryon Mass and Gradient Flow
The gradient-flow scale $w_0$ in lattice QCD is determined using the mass of the $Ω^-$ baryon to set the physical scale. Nine ensembles using the highly improved staggered quark (HISQ) action with lattice spacings of 0.15 fm down to 0.04 fm are used, seven of which have nearly physical light-quark masses. Electromagnetic corrections to the $Ω^-$ mass are defined in order to compute a pure-QCD $Ω$ mass. The final result is $w_0 = 0.17187(68)$ fm, corresponding to a relative uncertainty of 0.4…