Tootfinder

Origin of pressure-flow non-linearity in two-phase intermittent flow in porous media
Paolo Botticini, Davide Picchi, Santanu Sinha, Alex Hansen
https://arxiv.org/abs/2510.12588 …

Origin of pressure-flow non-linearity in two-phase intermittent flow in porous media
This study presents a first-principles model to predict the two-phase pressure drop in gas-liquid intermittent flow through round capillaries, which serve as the simplest analogous of a porous medium. Building upon the classical capillary flow theory, we derive a model for a train of elongated bubbles, and rigorously quantify its validity in terms of the dimensionless parameters of the problem (capillary number, number of bubbles, gas volume fraction, and channel length-to-diameter ratio). The …

from my link log —
Notes on gamma correction.
https://poniesandlight.co.uk/reflect/gamma/
saved 2025-12-12 https://dotat.at/:/BAJDJ.…

Notes on Gamma
The nuance between seeing and perceiving, linearity and non-linearity, what EOTFs are – and how to draw nice smooth gradients against the odds

Who Said Neural Networks Aren't Linear?
Nimrod Berman, Assaf Hallak, Assaf Shocher
https://arxiv.org/abs/2510.08570 https://arxiv.org/pdf/2510.08570

Who Said Neural Networks Aren't Linear?
Neural networks are famously nonlinear. However, linearity is defined relative to a pair of vector spaces, $f$$:$$X$$\to$$Y$. Is it possible to identify a pair of non-standard vector spaces for which a conventionally nonlinear function is, in fact, linear? This paper introduces a method that makes such vector spaces explicit by construction. We find that if we sandwich a linear operator $A$ between two invertible neural networks, $f(x)=g_y^{-1}(A g_x(x))$, then the corresponding vector spaces $…

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Effective Field Theory Perspective On King Non-linearity
Beno\^it Assi, Sam Carey, Sebastian J\"ager, Gabriel Lee, Gil Paz, Gilad Perez, Jure Zupan

Local limit of focusing discrete NLS
Kesav Krishnan, Gourab Ray
https://arxiv.org/abs/2509.25759 https://arxiv.org/pdf/2509.25759

Local limit of focusing discrete NLS
We examine the behavior of a function sampled from the invariant measure associated to the focusing discrete Non Linear Schrödinger equation, defined on a discrete torus of dimension $d \geq 3$, and nonlinearity parameter $p>4$, in the infinite volume limit. The Gibbs measure has two parameters, the inverse temperature and the strength of the non linearity, and the scaling is such that the non linear and linear parts of the Hamiltonian contribute on the same scale. It was shown by Dey, Kirkpat…

Random Feature Spiking Neural Networks
Maximilian Gollwitzer, Felix Dietrich
https://arxiv.org/abs/2510.01012 https://arxiv.org/pdf/2510.01012

Random Feature Spiking Neural Networks
Spiking Neural Networks (SNNs) as Machine Learning (ML) models have recently received a lot of attention as a potentially more energy-efficient alternative to conventional Artificial Neural Networks. The non-differentiability and sparsity of the spiking mechanism can make these models very difficult to train with algorithms based on propagating gradients through the spiking non-linearity. We address this problem by adapting the paradigm of Random Feature Methods (RFMs) from Artificial Neural Ne…

Morphologies of a sagging elastica with intrinsic sensing and actuation
Vishnu Deo Mishra, S Ganga Prasath
https://arxiv.org/abs/2509.17572 https://arxiv.o…

Morphologies of a sagging elastica with intrinsic sensing and actuation
The morphology of a slender soft-robot can be modified by sensing its shape via sensors and exerting moments via actuators embedded along its body. The actuating moments required to morph these soft-robots to a desired shape are often difficult to compute due to the geometric non-linearity associated with the structure, the errors in modeling the experimental system, and the limitations in sensing and feedback/actuation capabilities. In this article, we explore the effect of a simple feedback s…

Activation Function Design Sustains Plasticity in Continual Learning
Lute Lillo, Nick Cheney
https://arxiv.org/abs/2509.22562 https://arxiv.org/pdf/2509.22…

Activation Function Design Sustains Plasticity in Continual Learning
In independent, identically distributed (i.i.d.) training regimes, activation functions have been benchmarked extensively, and their differences often shrink once model size and optimization are tuned. In continual learning, however, the picture is different: beyond catastrophic forgetting, models can progressively lose the ability to adapt (referred to as loss of plasticity) and the role of the non-linearity in this failure mode remains underexplored. We show that activation choice is a primar…

On a class of thin obstacle-type problems for the bi-Laplacian operator
Donatella Danielli, Giovanni Gravina
https://arxiv.org/abs/2509.11372 https://arxiv…

On a class of thin obstacle-type problems for the bi-Laplacian operator
This paper investigates the regularity of solutions and structural properties of the free boundary for a class of fourth-order elliptic problems with Neumann-type boundary conditions. The singular and degenerate elliptic operators studied naturally emerge from the extension procedure for higher-order fractional powers of the Laplacian, while the choice of non-linearity considered encompasses two-phase boundary obstacle problems as a special case. After establishing local regularity properties o…