Tootfinder

macOS 26 Tahoe review: Liquid Glass has small regressions in usability and accessibility but the upgrade has enough iterative changes like automated Shortcuts (Andrew Cunningham/Ars Technica)
https://arstechnica.com/gadgets/2025/09/macos-26-tahoe-the-ars-techn…

macOS 26 Tahoe: The Ars Technica Review
Liquid Glass brings translucent sheen to the typical batch of iterative changes.

Inverse Synthetic Aperture Radar, Radar Cross Section, and Iterative Smooth Reweighting $\ell_1$-minimization
Christer Larsson
https://arxiv.org/abs/2508.10536 https://

Inverse Synthetic Aperture Radar, Radar Cross Section, and Iterative Smooth Reweighting $\ell_1$-minimization
Radar Cross Section measurement data is often analyzed using Inverse Synthetic Aperture Radar images. This paper compares backprojection and iterative smooth reweighted $\ell_1$-minimization as methods to analyze radar cross section measurements and extract radar cross section for parts of the measured object. The main conclusion is that using backprojection images to extract RCS is robust and accurate but is more limited by the resolution than iterative smooth reweighted $\ell_1$-minimization.…

An efficient iteration method to reconstruct the drift term from the final measurement
Dakang Cen, Wenlong Zhang, Zhidong Zhang
https://arxiv.org/abs/2510.10940 https://

An efficient iteration method to reconstruct the drift term from the final measurement
This work investigates the inverse drift problem in the one-dimensional parabolic equation with the final time data. The authors construct an operator first, whose fixed points are the unknown drift, and then apply it to prove the uniqueness. The proof of uniqueness contains an iteration converging to the drift, which inspires the numerical algorithm. To handle the ill-posedness of the inverse problem, the authors add the mollification on the data first in the iterative algorithm, and then prov…

Iterative polynomial regularisation of $\text{P}_{\text{IV}}$-type: Hamiltonian systems and Newton polygons
Marta Dell'Atti, Galina Filipuk
https://arxiv.org/abs/2510.12378 …

Iterative polynomial regularisation of $\text{P}_{\text{IV}}$-type: Hamiltonian systems and Newton polygons
We study several polynomial Hamiltonian systems of $\text{P}_{\text{IV}}$-type (including the mixed case quasi-$\text{P}_{\text{IV}}$), and show that via the iterative process of polynomial regularisation, it is possible to identify the "minimal" Hamiltonian system. The selected Hamiltonian function is associated with the Newton polygon with minimal area and smallest highest total degree.

Iterative Data Curation with Theoretical Guarantees
V\"ain\"o Yrj\"an\"ainen Johan Jonasson, M{\aa}ns Magnusson
https://arxiv.org/abs/2510.11428 https://

Iterative Data Curation with Theoretical Guarantees
In recent years, more and more large data sets have become available. Data accuracy, the absence of verifiable errors in data, is crucial for these large materials to enable high-quality research, downstream applications, and model training. This results in the problem of how to curate or improve data accuracy in such large and growing data, especially when the data is too large for manual curation to be feasible. This paper presents a unified procedure for iterative and continuous improvement …

Random Nonlinear Fusion Frames from Averaged Operator Iterations
James Tian
https://arxiv.org/abs/2509.09927 https://arxiv.org/pdf/2509.09927

Random Nonlinear Fusion Frames from Averaged Operator Iterations
We study random iterations of averaged operators in Hilbert spaces and prove that the associated residuals converge exponentially fast, both in expectation and almost surely. Our results provide quantitative bounds in terms of a single geometric parameter, giving sharp control of convergence rates under minimal assumptions. As an application, we introduce the concept of random nonlinear fusion frames. Here the atoms are generated dynamically from the residuals of the iteration and yield exact s…

On the Consistency and Performance of the Iterative Bayesian Update
Ehab ElSalamouny, Catuscia Palamidessi
https://arxiv.org/abs/2508.09980 https://arxiv.o…

On the Consistency and Performance of the Iterative Bayesian Update
For many social, scientific, and commercial purposes, it is often important to estimate the distribution of the users' data regarding a sensitive attribute, e.g., their ages, locations, etc. To allow this estimation while protecting the users' privacy, every user applies a local privacy protection mechanism that releases a noisy (sanitized) version of their original datum to the data collector; then the original distribution is estimated using one of the known methods, such as the matrix invers…

An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise
Johanna D\"ungler, Amartya Sanyal
https://arxiv.org/abs/2508.10879 https://

An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise
Given $n$ i.i.d. random matrices $A_i \in \mathbb{R}^{d \times d}$ that share a common expectation $Σ$, the objective of Differentially Private Stochastic PCA is to identify a subspace of dimension $k$ that captures the largest variance directions of $Σ$, while preserving differential privacy (DP) of each individual $A_i$. Existing methods either (i) require the sample size $n$ to scale super-linearly with dimension $d$, even under Gaussian assumptions on the $A_i$, or (ii) introduce excessiv…

Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference
Zhanghan Wang, Ding Ding, Hang Zhu, Haibin Lin, Aurojit Panda
https://arxiv.org/abs/2508.09505

Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference
Distributed machine learning training and inference is common today because today's large models require more memory and compute than can be provided by a single GPU. Distributed models are generally produced by programmers who take a sequential model specification and apply several distribution strategies to distribute state and computation across GPUs. Unfortunately, bugs can be introduced in the process, and a distributed model implementation's outputs might differ from the sequential model'…

A Generalized Alternating Anderson Acceleration Method
Yunhui He, Santolo Leveque
https://arxiv.org/abs/2508.10158 https://arxiv.org/pdf/2508.10158

A Generalized Alternating Anderson Acceleration Method
In this work, we propose a generalized alternating Anderson acceleration method, a periodic scheme composed of $t$ fixed-point iteration steps, interleaved with $s$ steps of Anderson acceleration with window size $m$, to solve linear and nonlinear problems. This allows flexibility to use different combinations of fixed-point iteration and Anderson iteration. We present a convergence analysis of the proposed scheme for accelerating the Richardson iteration in the linear case, with a focus on spe…