Tootfinder

Decidability of multiplicative matrix equations and related Diophantine problems
Sebastian Heintze, Armand Noubissie, Robert F. Tichy
https://arxiv.org/abs/2506.03932

Decidability of multiplicative matrix equations and related Diophantine problems
Some new decidability results for multiplicative matrix equations over algebraic number fields are established. In particular, special instances of the so-called knapsack problem are considered. The proofs are based on effective methods for Diophantine problems in finitely generated domains as presented in the recent book of Evertse and Györy. The focus lies on explicit bounds for the size of the solutions in terms of heights as well as on bounds for the number of solutions. This approach also…

Large Neighborhood and Hybrid Genetic Search for Inventory Routing Problems
Jingyi Zhao, Claudia Archetti, Tuan Anh Pham, Thibaut Vidal
https://arxiv.org/abs/2506.03172

Large Neighborhood and Hybrid Genetic Search for Inventory Routing Problems
The inventory routing problem (IRP) focuses on jointly optimizing inventory and distribution operations from a supplier to retailers over multiple days. Compared to other problems from the vehicle routing family, the interrelations between inventory and routing decisions render IRP optimization more challenging and call for advanced solution techniques. A few studies have focused on developing large neighborhood search approaches for this class of problems, but this remains a research area with…

Symmetry breaking for local minimizers of a free discontinuity problem
Massimo Gobbino, Nicola Picenni
https://arxiv.org/abs/2506.05270 https://

Symmetry breaking for local minimizers of a free discontinuity problem
We study a functional defined on the class of piecewise constant functions, combining a jump penalization, which discourages discontinuities, with a fidelity term that penalizes deviations from a given linear function, called the forcing term. In one dimension, it is not difficult to see that local minimizers form staircases that approximate the forcing term. Here we show that in two dimensions symmetry breaking occurs, leading to the emergence of exotic minimizers whose level sets are not si…

This https://arxiv.org/abs/2412.18148 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media
Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs). However, the misuse of AIGTs could have profound implications for public opinion, such as spreading misinformation and manipulating narratives. Despite its importance, it remains unclear how prevalent AIGTs are on social media. To address this gap, this paper aims to quantify and monitor the AIGTs on online social media platforms. We first collect a dataset (SM-D) with around 2.4M posts from 3 major social…

This https://arxiv.org/abs/2412.09840 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

LAVA: Lifetime-Aware VM Allocation with Learned Distributions and Adaptation to Mispredictions
Scheduling virtual machines (VMs) on hosts in cloud data centers dictates efficiency and is an NP-hard problem with incomplete information. Prior work improved VM scheduling with predicted VM lifetimes. Our work further improves lifetime-aware scheduling using repredictions with lifetime distributions versus one-shot prediction. Our approach repredicts and adjusts VM and host lifetimes when incorrect predictions emerge. We also present novel approaches for defragmentation and regular system mai…

This https://arxiv.org/abs/2505.08740 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
Parametric differential equations of the form du/dt = f(u, x, t, p) are fundamental in science and engineering. While deep learning frameworks such as the Fourier Neural Operator (FNO) can efficiently approximate solutions, they struggle with inverse problems, sensitivity estimation (du/dp), and concept drift. We address these limitations by introducing a sensitivity-based regularization strategy, called Sensitivity-Constrained Fourier Neural Operators (SC-FNO). SC-FNO achieves high accuracy in…

This https://arxiv.org/abs/2406.18322 has been replaced.
initial toot: https://mastoxiv.page/@…

A new quadrature for the generalized hydrodynamics equation and absence of shocks in the Lieb-Liniger model
In conventional fluids, it is well known that Euler-scale equations are plagued by ambiguities and instabilities. Smooth initial conditions may develop shocks, and weak solutions, such as for domain wall initial conditions (the paradigmatic Riemann problem of hydrodynamics), are not unique. The absence of shock formation experimentally observed in quasi-one-dimensional cold-atomic gases, which are described by the Lieb-Liniger model, provides perhaps the strongest pointer to a modification of t…

Collisionless relaxation to quasi-steady state attractors in cold dark matter halos: origin of the universal NFW profile
Uddipan Banik, Amitava Bhattacharjee
https://arxiv.org/abs/2506.02104

Collisionless relaxation to quasi-steady state attractors in cold dark matter halos: origin of the universal NFW profile
Collisionless self-gravitating systems such as cold dark matter halos are known to harbor universal density profiles despite the intricate non-linear physics of hierarchical structure formation in the $Λ$CDM paradigm. The origin of these attractor states has been a persistent mystery, particularly because the physics of collisionless relaxation is not well understood. To solve this long-standing problem, we develop a self-consistent quasilinear theory in action-angle space for the collisionles…

Off-shell ambiguities in correlation functions: strategies to minimize them
R. Molina, E. Oset
https://arxiv.org/abs/2506.03669 https://

Off-shell ambiguities in correlation functions: strategies to minimize them
We face here the problem of uncertainties in correlation functions due to the freedom in the off-shell dependence of the wave functions, or equivalently, off-shell ambiguities in the scattering matrices. We make the study for the case of meson baryon interaction, choosing the $KΛ$, $K Σ$, $ηN$ coupled channels, which are related to the $N^*(1535)$ resonance. We find that using realistic interactions based on chiral dynamics the uncertainties are small, of the order of $2-3$~\%, but could be …

This https://arxiv.org/abs/2505.19552 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

On scalable and efficient training of diffusion samplers
We address the challenge of training diffusion models to sample from unnormalized energy distributions in the absence of data, the so-called diffusion samplers. Although these approaches have shown promise, they struggle to scale in more demanding scenarios where energy evaluations are expensive and the sampling space is high-dimensional. To address this limitation, we propose a scalable and sample-efficient framework that properly harmonizes the powerful classical sampling method and the diffu…