Tootfinder

This https://arxiv.org/abs/2505.24603 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique for multiple problems in data science and machine learning, with applications spanning computationally efficient optimization, coded computing, and federated learning. This operation also provides differential privacy guarantees due to its inherent randomness. In this work, we revisit this operation through the lens of Renyi Differential Privacy (RDP), providing a refined pri…

A fourth-order exponential time differencing scheme with real and distinct poles rational approximation for solving non-linear reaction-diffusion systems
Wisdom Kwame Attipoe, Andreas Kleefeld, Emmanuel Asante-Asamani
https://arxiv.org/abs/2507.01245

A fourth-order exponential time differencing scheme with real and distinct poles rational approximation for solving non-linear reaction-diffusion systems
A fourth-order, L-stable, exponential time differencing Runge-Kutta type scheme is developed to solve nonlinear systems of reaction diffusion equations with nonsmooth data. The new scheme, ETDRK4RDP, is constructed by approximating the matrix exponentials in the ETDRK4 scheme with a fourth order, L-acceptable, non-Padé rational function having real and distinct poles (RDP). Using RDP rational functions to construct the scheme ensures efficient damping of spurious oscillations arising from non-…

Getting the Windows dev box set up for scopehal testing.
The most important step...

A Convergent Primal-Dual Algorithm for Computing Rate-Distortion-Perception Functions
Chunhui Chen, Linyi Chen, Xueyan Niu, Hao Wu
https://arxiv.org/abs/2508.13486 https://

A Convergent Primal-Dual Algorithm for Computing Rate-Distortion-Perception Functions
Recent advances in Rate-Distortion-Perception (RDP) theory highlight the importance of balancing compression level, reconstruction quality, and perceptual fidelity. While previous work has explored numerical approaches to approximate the information RDP function, the lack of theoretical guarantees remains a major limitation, especially in the presence of complex perceptual constraints that introduce non-convexity and computational intractability. Inspired by our previous constrained Blahut-Arim…

Measuring Ransomware Lateral Movement Susceptibility via Privilege-Weighted Adjacency Matrix Exponentiation
Satyam Tyagi, Ganesh Murugesan
https://arxiv.org/abs/2508.21005 https…

Measuring Ransomware Lateral Movement Susceptibility via Privilege-Weighted Adjacency Matrix Exponentiation
Ransomware impact hinges on how easily an intruder can move laterally and spread to the maximum number of assets. We present a graph-theoretic method to measure lateral-movement susceptibility and estimate blast radius. We build a directed multigraph where vertices represent assets and edges represent reachable services (e.g., RDP/SSH) between them. We model lateral movement as a probabilistic process using a pivot potential factor $π(s)$ for each service. This allows us to iteratively compute…

A Nonconforming Finite Element Method for Elliptic Interface Problems on Locally Anisotropic Meshes
Hua Wang, Qichen Zhang
https://arxiv.org/abs/2506.15077

A Nonconforming Finite Element Method for Elliptic Interface Problems on Locally Anisotropic Meshes
We propose a new nonconforming \(P_1\) finite element method for elliptic interface problems. The method is constructed on a locally anisotropic mixed mesh, which is generated by fitting the interface through a simple connection of intersection points on an interface-unfitted background mesh, as introduced in \cite{Hu2021optimal}. We first establish interpolation error estimates on quadrilateral elements satisfying the regular decomposition property (RDP). Building on this, the main contributio…

Fast PET Reconstruction with Variance Reduction and Prior-Aware Preconditioning
Matthias J. Ehrhardt, Zeljko Kereta, Georg Schramm
https://arxiv.org/abs/2506.04976

Fast PET Reconstruction with Variance Reduction and Prior-Aware Preconditioning
We investigate subset-based optimization methods for positron emission tomography (PET) image reconstruction incorporating a regularizing prior. PET reconstruction methods that use a prior, such as the relative difference prior (RDP), are of particular relevance, as they are widely used in clinical practice and have been shown to outperform conventional early-stopped and post-smoothed ordered subsets expectation maximization (OSEM). Our study evaluates these methods on both simulated data and…