Tootfinder

Stellar Population Astrophysics (SPA) with the TNG: NLTE atmospheric parameters and abundances of giant stars in 33 Open Clusters
M. Dal Ponte, V. D'Orazi, A. Bragaglia, A. R. Casey, N. Storm, L. Spina, J. Alonso-Santiago, G. Andreuzzi, A. Frasca, J. Kos, S. Lucatello, D. Romano, A. Vallenari, N. Vernekar
https://arxiv.org/a…

Stellar Population Astrophysics (SPA) with the TNG: NLTE atmospheric parameters and abundances of giant stars in 33 Open Clusters
Open clusters serve as important tools for accurately studying the chemical evolution of the Milky Way. By combining precise chemical data from high-resolution spectra with information on their distances and ages, we can effectively uncover the processes that have shaped our Galaxy. This study aims to derive NLTE atmospheric parameters and chemical abundances for approximately one hundred giant stars across 33 open clusters with near-solar metallicity. The clusters span a wide range of ages, en…

A Systematic Particle Filter for Estimating Time-Varying Parameters in Advection-Diffusion Equations with Source Terms
Andrea Arnold
https://arxiv.org/abs/2508.12155 https://

A Systematic Particle Filter for Estimating Time-Varying Parameters in Advection-Diffusion Equations with Source Terms
Many real-world systems modeled using partial differential equations (PDEs) involve unknown parameters that must be estimated from limited, noisy system observations. While typically assumed to be constants, some of these unobserved parameters may vary with time. This work proposes a two-phase, offline-online numerical procedure for systematically estimating and quantifying uncertainty in time-varying parameters (TVPs) in time-dependent PDEs, specifically focusing on advection-diffusion models …

DiEP: Adaptive Mixture-of-Experts Compression through Differentiable Expert Pruning
Sikai Bai, Haoxi Li, Jie Zhang, Zicong Hong, Song Guo
https://arxiv.org/abs/2509.16105 https:…

DiEP: Adaptive Mixture-of-Experts Compression through Differentiable Expert Pruning
Despite the significant breakthrough of Mixture-of-Experts (MoE), the increasing scale of these MoE models presents huge memory and storage challenges. Existing MoE pruning methods, which involve reducing parameter size with a uniform sparsity across all layers, often lead to suboptimal outcomes and performance degradation due to varying expert redundancy in different MoE layers. To address this, we propose a non-uniform pruning strategy, dubbed \textbf{Di}fferentiable \textbf{E}xpert \textbf{P…

Variable selection for minimum-variance portfolios
Guilherme V. Moura, Andr\'e P. Santos, Hudson S. Torrent
https://arxiv.org/abs/2508.14986 https://ar…

Variable selection for minimum-variance portfolios
Machine learning (ML) methods have been successfully employed in identifying variables that can predict the equity premium of individual stocks. In this paper, we investigate if ML can also be helpful in selecting variables relevant for optimal portfolio choice. To address this question, we parameterize minimum-variance portfolio weights as a function of a large pool of firm-level characteristics as well as their second-order and cross-product transformations, yielding a total of 4,610 predicto…

Deterministic time rewinding of waves in time-varying media
Seulong Kim, Kihong Kim
https://arxiv.org/abs/2508.14241 https://arxiv.org/pdf/2508.14241

Deterministic time rewinding of waves in time-varying media
Temporal modulation of material parameters offers unprecedented control over wave dynamics, enabling phenomena beyond the capabilities of static systems. Here we introduce and analyze a robust mechanism for time rewinding, whereby a temporally evolved wave is fully restored to its original state through a carefully engineered sequence of temporal modulations. In electromagnetic systems, time rewinding emerges from impedance-matched or anti-matched hierarchical bilayer structures with matched mo…

From ANN to BNN: Inferring Reionization Parameters using Uncertainty-aware Emulators of 21-cm Summaries
Yashrajsinh Mahida, Sanjay Kumar Yadav, Suman Majumdar, Leon Noble, Chandra Shekhar Murmu, Saswata Dasgupta, Sohini Dutta, Himanshu Tiwari, Abinash Kumar Shaw
https://arxiv.org/abs/2508.13261

From ANN to BNN: Inferring Reionization Parameters using Uncertainty-aware Emulators of 21-cm Summaries
Inferring astrophysical parameters from radio interferometric observations of the redshifted 21-cm signal from the Epoch of Reionization (EoR) is a challenging yet crucial task. The 21-cm signal from EoR is expected to be highly non-Gaussian; therefore, we need to use higher-order statistics, e.g., bispectrum. Moreover, the forward modeling of the signal and its statistics for a varying set of model parameters requires rerunning the simulations many times, which is computationally very expensiv…

Replaced article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/1]:
- Moving horizon estimation for nonlinear systems with time-varying parameters
Julian D. Schiller, Matthias A. M\"uller

Deadbeat Robust Model Predictive Control for Linear Parameter-Varying Systems
Georg Schildbach, Hossam S. Abbas
https://arxiv.org/abs/2509.14885 https://ar…

Deadbeat Robust Model Predictive Control for Linear Parameter-Varying Systems
The concept of Deadbeat Robust Model Predictive Control (DRMPC) is to completely extinguish the effect of external disturbances within the first few steps of the prediction horizon. The benefit is that the remaining dynamics of the system can be planned with certainty. This means it is not necessary to employ a Robust Positive Invariant (RPI) set as a terminal condition, which is often complex or even intractable to compute. Recent work has shown that it is possible to obtain full system theore…

Direct data-driven interpolation and approximation of linear parameter-varying system trajectories
Chris Verhoek, Ivan Markovsky, Roland T\'oth
https://arxiv.org/abs/2508.11332

Direct data-driven interpolation and approximation of linear parameter-varying system trajectories
We consider the problem of estimating missing values in trajectories of linear parameter-varying (LPV) systems. We solve this interpolation problem for the class of shifted-affine LPV systems. Conditions for the existence and uniqueness of solutions are given and a direct data-driven algorithm for its computation is presented, i.e., the data-generating system is not given by a parametric model but is implicitly specified by data. We illustrate the applicability of the proposed solution on illus…

Overprinting with Tomographic Volumetric Additive Manufacturing
Felix Wechsler, Viola Sgarminato, Riccardo Rizzo, Baptiste Nicolet, Wenzel Jakob, Christophe Moser
https://arxiv.org/abs/2507.13842

Overprinting with Tomographic Volumetric Additive Manufacturing
Tomographic Volumetric Additive Manufacturing (TVAM) is a light-based 3D printing technique capable of producing centimeter-scale objects within seconds. A key challenge lies in the calculation of projection patterns under non-standard conditions, such as the presence of occlusions and materials with diverse optical properties, including varying refractive indices or scattering surfaces. This work focuses on demonstrating a wide variety of overprinting scenarios. First, utilizing a telecentric …