Tootfinder

Mysterious Crime Spree Targeted National Guard Equipment Stashes
https://www.wired.com/story/mysterious-crime-spree-targeted-national-guard-equipment-stashes/

Mysterious Crime Spree Targeted National Guard Equipment Stashes
A string of US armory break-ins, kept quiet by authorities for months, points to a growing security crisis—and signs of an inside job.

Nvidia says its GPUs do not contain backdoors, kill switches, or spyware, and hard-coded, single-point controls like kill switches undermine trust in US tech (David Reber Jr/NVIDIA)
https://blogs.nvidia.com/blog/no-backdoors-no-kill-switches-no-spyware/

No Backdoors. No Kill Switches. No Spyware.
To mitigate the risk of misuse, some pundits and policymakers propose requiring hardware “kill switches” or built-in controls that can remotely disable GPUs without user knowledge and consent. Some suspect they might already exist. NVIDIA GPUs do not and should not have kill switches and backdoors.

Spontaneous stochasticity in the fluctuating Navier-Stokes equations on a logarithmic lattice
Erika Ortiz, Ciro S. Campolina, Alexei A. Mailybaev
https://arxiv.org/abs/2507.03196 …

Spontaneous stochasticity in the fluctuating Navier-Stokes equations on a logarithmic lattice
The predictability of turbulent flows remains a challenging problem for mathematicians, physicists, and meteorologists. In this context, we consider the 3D incompressible Navier-Stokes equations with small-scale random forcing on logarithmic lattices in Fourier space. Our goal is to probe the phenomenon of spontaneous stochasticity in this system, which means that its solutions remain stochastic in the limit of vanishing viscosity and noise. For this, we consider numerical simulations with incr…

A Direct Reduction from Stochastic Parity Games to Simple Stochastic Games
Rapha\"el Berthon, Joost-Pieter Katoen, Zihan Zhou
https://arxiv.org/abs/2506.06223

A Direct Reduction from Stochastic Parity Games to Simple Stochastic Games
Significant progress has been recently achieved in developing efficient solutions for simple stochastic games (SSGs), focusing on reachability objectives. While reductions from stochastic parity games (SPGs) to SSGs have been presented in the literature through the use of multiple intermediate game models, a direct and simple reduction has been notably absent. This paper introduces a novel and direct polynomial-time reduction from quantitative SPGs to quantitative SSGs. By leveraging a gadget-b…

Computer-aided analyses of stochastic first-order methods, via interpolation conditions for stochastic optimization
Anne Rubbens, S\'ebastien Colla, Julien M. Hendrickx
https://arxiv.org/abs/2507.05466

Computer-aided analyses of stochastic first-order methods, via interpolation conditions for stochastic optimization
This work proposes a framework, embedded within the Performance Estimation framework (PEP), for obtaining worst-case performance guarantees on stochastic first-order methods. Given a first-order method, a function class, and a noise model with prescribed expectation and variance properties, we present a range of semidefinite programs (SDPs) of increasingly large size, whose solutions yield increasingly strong convergence guarantees on the problem. Eventually, we propose SDPs whose size depends …

Numerical analysis of the stochastic Navier-Stokes equations
Dominic Breit, Andreas Prohl, J\"orn Wichman
https://arxiv.org/abs/2508.05564 https://arx…

Numerical analysis of the stochastic Navier-Stokes equations
The developments over the last five decades concerning numerical discretisations of the incompressible Navier--Stokes equations have lead to reliable tools for their approximation: those include stable methods to properly address the incompressibility constraint, stable discretisations to account for convection dominated problems, efficient time (splitting) methods, and methods to tackle their nonlinear character. While these tools may successfully be applied to reliably simulate even more comp…

Emergent dynamical scaling in the inviscid limit of 3D stochastic Navier-Stokes equation with thermal noise
Liubov Gosteva, Marc Brachet, L\'eonie Canet
https://arxiv.org/abs/2507.05811

Emergent dynamical scaling in the inviscid limit of 3D stochastic Navier-Stokes equation with thermal noise
Spectrally truncated three-dimensional (3D) Euler flows reach an absolute equilibrium. Yet they show non-trivial temporal correlations, similar to the ones of turbulent flows. In this work, we investigate Navier-Stokes equation in equilibrium conditions, both at finite viscosity and in the inviscid limit. We determine the space-time velocity correlations, using functional renormalisation group and direct numerical simulations. We characterise the crossover in the dynamical scaling from $τ\sim …

Model order reduction techniques for the stochastic finite volume method
Ray Qu, Jesse Chan, Svetlana Tokareva
https://arxiv.org/abs/2507.05091 https://

Model order reduction techniques for the stochastic finite volume method
The stochastic finite volume method (SFV method) is a high-order accurate method for uncertainty quantification (UQ) in hyperbolic conservation laws. However, the computational cost of SFV method increases for high-dimensional stochastic parameter spaces due to the curse of dimensionality. To address this challenge, we incorporate interpolation-based reduced order modeling (ROM) techniques that reduce the cost of computing stochastic integrals in SFV method. Further efficiency gains are achieve…

Computing stabilizing feedback gains for stochastic linear systems via policy iteration method
Xinpei Zhang, Guangyan Jia
https://arxiv.org/abs/2508.05214 https://

Computing stabilizing feedback gains for stochastic linear systems via policy iteration method
In recent years, stabilizing unknown dynamical systems has became a critical problem in control systems engineering. Addressing this for linear time-invariant (LTI) systems is an essential fist step towards solving similar problems for more complex systems. In this paper, we develop a model-free reinforcement learning algorithm to compute stabilizing feedback gains for stochastic LTI systems with unknown system matrices. This algorithm proceeds by solving a series of discounted stochastic linea…

Relationship between maximum principle and dynamic programming principle for recursive optimal control problem of stochastic evolution equations
Ying Hu, Guomin Liu, Shanjian Tang
https://arxiv.org/abs/2507.06118

Relationship between maximum principle and dynamic programming principle for recursive optimal control problem of stochastic evolution equations
This paper aims to study the relationship between the maximum principle and the dynamic programming principle for recursive optimal control problem of stochastic evolution equations, where the control domain is not necessarily convex and the value function may be nonsmooth. By making use of the notion of conditionally expected operator-valued backward stochastic integral equations, we establish a connection between the first and second-order adjoint processes in MP and the general derivatives o…