Tootfinder

Measuring pulsar profile variations with 2-D Gaussian process regression
Michael J. Keith, Ren\'ee Spiewak, Andrew G. Lyne, Patrick Weltevrede, Danai Antonopoulou, Ben Stappers
https://arxiv.org/abs/2505.23413

Measuring pulsar profile variations with 2-D Gaussian process regression
Time-correlated variations in the pulse profiles of radio pulsars provide insights into changes in their magnetospheres. For a small number of pulsars (~20), these variations have been shown to correlate with spin-down rate. Many of these profile changes involve small (few percent) variations in the relative intensity of different profile components, and hence tools such as Gaussian process regression have been employed to separate the time-correlated profile variation from intrinsic noise. In …

This https://arxiv.org/abs/2505.22398 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Numerical Optimization Strategies for the Variational Hamiltonian Ansatz in Noisy Quantum Environments
We conduct a benchmark of eight optimization algorithms for variational quantum chemistry using the tVHA, evaluating performance on $H_2$, $H_4$, and $LiH$ (in both full and active spaces) under noiseless and sampling noise conditions. Sampling noise fundamentally alters optimizer behavior, with gradient-based methods performing best in ideal conditions, while population-based algorithms, such as CMA-ES, show greater resilience under noise. Hartree-Fock initialization reduces the number of func…

This https://arxiv.org/abs/2303.00847 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Analysis of Four-Dimensional Variational Data Assimilation Problems in Low Regularity Spaces
We carry out a rigorous analysis of four-dimensional variational data assimilation ($4D$-VAR) problems for linear and semilinear parabolic partial differential equations. Continuity of the state with respect to the spatial variable is required since pointwise observations of the state variable appear in the cost functional. Using maximal parabolic regularity tools, we prove this regularity for initial conditions with $L^β$-regularity guaranteed by control constraints, rather than Sobolev regul…

This https://arxiv.org/abs/2407.06734 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Variation of the one-dimensional centered maximal operator on simple functions with gaps between pieces
Let $M$ denote the centered Hardy--Littlewood operator on $\mathbb{R}$. We prove that \[ {\rm Var} (Mf)\le {\rm Var} (f) - \frac12\big| |f(\infty)|-|f(-\infty)|\big| \] for piecewise constant functions $f$ with nonzero and zero values alternating. The above inequality strengthens a recent result of Bilz and Weigt \cite{BW} proved for indicator functions of bounded variation vanishing at $\pm\infty$. We conjecture that the inequality holds for all functions of bound…

This https://arxiv.org/abs/2505.17418 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI
Generative AI (genAI) tools are advertised as productivity aids. Yet, issues related to miscalibrated trust and usage friction continue to hinder their adoption. Additionally, AI can be exclusionary, failing to support diverse users adequately, further exacerbating these concerns. One such aspect of diversity is cognitive diversity -- variations in users' cognitive styles -- that leads to divergence in interaction styles. When an individual's cognitive styles are unsupported, it creates additio…

Standing Ovation für @… für ihren Talk über Medienberichterstattung über den Krieg im Nahen Osten und das verloren gegangene Vertrauen in diese Berichterstattung. Und so verdient. Folgt ihr!
#republica

This https://arxiv.org/abs/2303.00847 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Analysis of Four-Dimensional Variational Data Assimilation Problems in Low Regularity Spaces
We carry out a rigorous analysis of four-dimensional variational data assimilation ($4D$-VAR) problems for linear and semilinear parabolic partial differential equations. Continuity of the state with respect to the spatial variable is required since pointwise observations of the state variable appear in the cost functional. Using maximal parabolic regularity tools, we prove this regularity for initial conditions with $L^β$-regularity guaranteed by control constraints, rather than Sobolev regul…

Direct approach to approximate conservation laws
M. Gorgone, G. Inferrera
https://arxiv.org/abs/2505.23390 https://arxiv.org/pdf/2505…

Direct approach to approximate conservation laws
In this paper, non-variational systems of differential equations containing small terms are considered, and a consistent approach for deriving approximate conservation laws through the introduction of approximate Lagrange multipliers is developed. The proposed formulation of the approximate direct method starts by assuming the Lagrange multipliers to be dependent on the small parameter; then, by expanding the dependent variables in power series of the small parameter, we consider the consistent…

This https://arxiv.org/abs/2406.00937 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Stochastic Variance-Reduced Forward-Reflected-Backward Splitting Methods for Nonmonotone Generalized Equations
We develop two novel stochastic variance-reduction methods to approximate solutions of a class of nonmonotone [generalized] equations. Our algorithms leverage a new combination of ideas from the forward-reflected-backward splitting method and a class of unbiased variance-reduced estimators. We construct two new stochastic estimators within this class, inspired by the well-known SVRG and SAGA estimators. These estimators significantly differ from existing approaches used in minimax and variation…

This https://arxiv.org/abs/2406.00937 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Stochastic Variance-Reduced Forward-Reflected-Backward Splitting Methods for Nonmonotone Generalized Equations
We develop two novel stochastic variance-reduction methods to approximate solutions of a class of nonmonotone [generalized] equations. Our algorithms leverage a new combination of ideas from the forward-reflected-backward splitting method and a class of unbiased variance-reduced estimators. We construct two new stochastic estimators within this class, inspired by the well-known SVRG and SAGA estimators. These estimators significantly differ from existing approaches used in minimax and variation…