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Crosslisted article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Optimal control of Volterra integral diffusions and application to contract theory
Dylan Possama\"i, Mehdi Talbi
https://arxiv.org/abs/2511.09701 https://mastoxiv.page/@arXiv_mathPR_bot/115547093766733637
- Generalized infinite dimensional Alpha-Procrustes based geometries
Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra
https://arxiv.org/abs/2511.09801 https://mastoxiv.page/@arXiv_statML_bot/115547135711272091
- Sample Complexity of Quadratically Regularized Optimal Transport
Alberto Gonz\'alez-Sanz, Eustasio del Barrio, Marcel Nutz
https://arxiv.org/abs/2511.09807 https://mastoxiv.page/@arXiv_mathST_bot/115546975796760368
- On the Convergence of Overparameterized Problems: Inherent Properties of the Compositional Struct...
Arthur Castello Branco de Oliveira, Dhruv Jatkar, Eduardo Sontag
https://arxiv.org/abs/2511.09810 https://mastoxiv.page/@arXiv_csLG_bot/115547543989283588
- Implicit Multiple Tensor Decomposition
Kunjing Yang, Libin Zheng, Minru Bai
https://arxiv.org/abs/2511.09916 https://mastoxiv.page/@arXiv_mathNA_bot/115547169767663335
- Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies
Takehiro Tottori, Tetsuya J. Kobayashi
https://arxiv.org/abs/2511.10184 https://mastoxiv.page/@arXiv_physicsbioph_bot/115546979073652600
- Zeroes and Extrema of Functions via Random Measures
Athanasios Christou Micheas
https://arxiv.org/abs/2511.10293 https://mastoxiv.page/@arXiv_statME_bot/115547493525198835
- Operator Models for Continuous-Time Offline Reinforcement Learning
Nicolas Hoischen, Petar Bevanda, Max Beier, Stefan Sosnowski, Boris Houska, Sandra Hirche
https://arxiv.org/abs/2511.10383 https://mastoxiv.page/@arXiv_statML_bot/115547254989932993
- On topological properties of closed attractors
Wouter Jongeneel
https://arxiv.org/abs/2511.10429 https://mastoxiv.page/@arXiv_mathDS_bot/115547276594491411
- Learning parameter-dependent shear viscosity from data, with application to sea and land ice
Gonzalo G. de Diego, Georg Stadler
https://arxiv.org/abs/2511.10452 https://mastoxiv.page/@arXiv_mathNA_bot/115547323782478749
- Formal Verification of Control Lyapunov-Barrier Functions for Safe Stabilization with Bounded Con...
Jun Liu
https://arxiv.org/abs/2511.10510 https://mastoxiv.page/@arXiv_eessSY_bot/115547429321496393
- Direction-of-Arrival and Noise Covariance Matrix joint estimation for beamforming
Vitor Gelsleichter Probst Curtarelli
https://arxiv.org/abs/2511.10639 https://mastoxiv.page/@arXiv_eessAS_bot/115547188796143762
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Exact bounds for efficient consistent matrices obtained from a reciprocal matrix
Susana Furtado, Charles Johnson
https://arxiv.org/abs/2510.12358 https://a…

Exact bounds for efficient consistent matrices obtained from a reciprocal matrix
For a given reciprocal matrix A, we give a union of matrix intervals in which any consistent matrix obtained from an efficient vector for A lies, and, conversely, any consistent matrix in this union comes from an efficient vector for A. The maximal sets of entries in the lower and upper bound matrices of each interval that are attainable by some consistent matrix in the interval are described. This allows us to understand which subsets of the alternatives lie above which other subsets in all ef…

A New Method of Constructing Hadamard Matrices, Circulant Hadamard Matrices, CZCS, GCS, CCC, and CZCSS
Piyush Priyanshu, Sudhan Majhi, Subhabrata Paul
https://arxiv.org/abs/2510.12315

A New Method of Constructing Hadamard Matrices, Circulant Hadamard Matrices, CZCS, GCS, CCC, and CZCSS
A Hadamard matrix $H$ is a square matrix of order $n$ with entries $\pm 1$, such that $HH^\top=nI_{n}$, where $I_n$ is an identity matrix of order $n$. A circulant Hadamard matrix $H$ is a Hadamard matrix that has rows of entries in cyclic order. There exist only $8$ circulant Hadamard matrices of order 4, and here, we provide a novel construction of all such $8$ circulant Hadamard matrices using a linear operator and generalized Boolean function (GBF). The constructed circulant Hadamard matric…

Does Weighting Improve Matrix Factorization for Recommender Systems?
Alex Ayoub, Samuel Robertson, Dawen Liang, Harald Steck, Nathan Kallus
https://arxiv.org/abs/2510.10440 http…

Does Weighting Improve Matrix Factorization for Recommender Systems?
Matrix factorization is a widely used approach for top-N recommendation and collaborative filtering. When implemented on implicit feedback data (such as clicks), a common heuristic is to upweight the observed interactions. This strategy has been shown to improve performance for certain algorithms. In this paper, we conduct a systematic study of various weighting schemes and matrix factorization algorithms. Somewhat surprisingly, we find that training with unweighted data can perform comparably …

Matrix Random Walks and the Lima Bean Law
Bruce K. Driver, Brian C. Hall, Ching Wei Ho, Todd Kemp, Yuriy Nemish, Evangelos A. Nikitopoulos, Felix Parraud
https://arxiv.org/abs/2510.10712

Matrix Random Walks and the Lima Bean Law
A matrix random walk is a stochastic process of the form $B_k = (I+A_1)\cdots(I+A_k)$ where $A_j$ are independent ``step'' matrices in $\mathrm{M}_N(\mathbb{C})$. With the right entry-covariance, a rescaled matrix random walk converges to Brownian motion $B(t)$ on a matrix Lie group. In this paper, we study the eigenvalues of such rescaled matrix random walks, as $N\to\infty$ and $k\to\infty$. The standard Brownian motion $W(t)$ on $\mathrm{M}_N(\mathbb{C})$ has independent Gaussian entries a…

ADiP: Adaptive Precision Systolic Array for Matrix Multiplication Acceleration
Ahmed J. Abdelmaksoud, Cristian Sestito, Shiwei Wang, Themis Prodromakis
https://arxiv.org/abs/2510.10623

ADiP: Adaptive Precision Systolic Array for Matrix Multiplication Acceleration
Transformers are at the core of modern AI nowadays. They rely heavily on matrix multiplication and require efficient acceleration due to their substantial memory and computational requirements. Quantization plays a vital role in reducing memory usage, and can be exploited for computations by designing reconfigurable architectures that enhance matrix multiplication by dynamically adjusting the precision. This paper proposes ADiP, a novel adaptive-precision systolic array architecture designed fo…

Reduced Density Matrix Functional Theory And A Reduced Formulation Of Density Functional Theory
H{\aa}kon Richard Fredheim, Simen Kvaal
https://arxiv.org/abs/2510.12242 https://…

Reduced Density Matrix Functional Theory And A Reduced Formulation Of Density Functional Theory
A mathematical framework for reduced density matrix functional theory (RDMFT) is proposed. The work is inspired by and generalizes the work by E.H.~Lieb [E.H. Lieb, Int. J. Quant. Chem. 24(1983), pp.243--277] on density-functional theory (DFT). We introduce a Banach space for density matrices with finite kinetic energy. The dual space is a rich class of single-particle potentials, i.e., Hermitian forms. The ground state energy of an $N$-fermion system with external forces given by any such Herm…

Dimension-Free Minimax Rates for Learning Pairwise Interactions in Attention-Style Models
Shai Zucker, Xiong Wang, Fei Lu, Inbar Seroussi
https://arxiv.org/abs/2510.11789 https:…

Dimension-Free Minimax Rates for Learning Pairwise Interactions in Attention-Style Models
We study the convergence rate of learning pairwise interactions in single-layer attention-style models, where tokens interact through a weight matrix and a non-linear activation function. We prove that the minimax rate is $M^{-\frac{2β}{2β+1}}$ with $M$ being the sample size, depending only on the smoothness $β$ of the activation, and crucially independent of token count, ambient dimension, or rank of the weight matrix. These results highlight a fundamental dimension-free statistical efficie…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/7]:
- CAMERA: Multi-Matrix Joint Compression for MoE Models via Micro-Expert Redundancy Analysis
Yuzhuang Xu, Xu Han, Yuanchi Zhang, Yixuan Wang, Yijun Liu, Shiyu Ji, Qingfu Zhu, Wanxiang Che

FACE: Faithful Automatic Concept Extraction
Dipkamal Bhusal, Michael Clifford, Sara Rampazzi, Nidhi Rastogi
https://arxiv.org/abs/2510.11675 https://arxiv.…

FACE: Faithful Automatic Concept Extraction
Interpreting deep neural networks through concept-based explanations offers a bridge between low-level features and high-level human-understandable semantics. However, existing automatic concept discovery methods often fail to align these extracted concepts with the model's true decision-making process, thereby compromising explanation faithfulness. In this work, we propose FACE (Faithful Automatic Concept Extraction), a novel framework that augments Non-negative Matrix Factorization (NMF) with…

Rotation of crystal seed during early stages of growth reveals the anisotropy of glass matrix
R. Thapa, E. Mustermann, H. Jain, V. Dierolf, M. E. McKenzie
https://arxiv.org/abs/2510.09807

Rotation of crystal seed during early stages of growth reveals the anisotropy of glass matrix
Rotation of crystal seed during the early stages of growth in a glass matrix has been observed due to some torque, contradicting the expectations from the isotropic, uniform structure of the surrounding amorphous matrix. We establish an atomistic origin of this new phenomenon from molecular dynamics simulations using LiNbO3 and LiNbO3-SiO2 glasses as model systems. Effectively, it arises due to non-uniform forces on the seed from the surrounding glass, which appears inhomogeneous and anisotropi…

A GPU-Accelerated Matrix-Free FAS Multigrid Solver for Navier-Stokes Equations with Memory-Efficient Implementations
Jiale Meng, Shuqi Tang, Steven M. Wise, Zhenlin Guo
https://arxiv.org/abs/2510.11152

A GPU-Accelerated Matrix-Free FAS Multigrid Solver for Navier-Stokes Equations with Memory-Efficient Implementations
We develop a matrix-free Full Approximation Storage (FAS) multigrid solver based on staggered finite differences and implemented on GPU in MATLAB. To enhance performance, intermediate variables are reused, and an X-shape Multi-Color Gauss-Seidel (X-MCGS) smoother is introduced, which eliminates conditional branching by partitioning the grid into four submatrices. Restriction and prolongation operators are also GPU-accelerated. Convergence tests verify robustness and accuracy, while benchmarks s…

A GPU-resident Memory-Aware Algorithm for Accelerating Bidiagonalization of Banded Matrices
Evelyne Ringoot, Rabab Alomairy, Alan Edelman
https://arxiv.org/abs/2510.12705 https:…

A GPU-resident Memory-Aware Algorithm for Accelerating Bidiagonalization of Banded Matrices
The reduction of a banded matrix to a bidiagonal form is a crucial step in the Singular Value Decomposition (SVD), a cornerstone of scientific computing and AI. Despite being a highly parallel algorithm, it was previously believed to be unsuitable for GPU computation because it is memory bandwidth-bound. Recent developments in GPU hardware, including larger L1 memory per Streaming Multiprocessor/Compute Unit, have changed that. We present the first GPU algorithm for reducing a banded matrix to …

Scattering in the dual regime of Yang-Baxter deformed O(2N) sigma models
Alexey Bychkov, Boris Nekrasov
https://arxiv.org/abs/2510.11429 https://arxiv.org/…

Scattering in the dual regime of Yang-Baxter deformed O(2N) sigma models
We continue to explore the previously suggested dual regime of Yang-Baxter (YB) deformed $\mathrm{O}(2N)$ sigma models, which is a new one-parametric deformation of the $\mathrm{O}(2N)$ model. It can be obtained from the conventional YB deformed $\mathrm{O}(2N+2)$ sigma model by freezing two isometries. The scattering matrix in the non-deformed $\mathrm{O}(n)$ model is known coincide with the rational $\mathfrak{so}_n$-invariant $R-$matrix. For $n = 2N$ this rational solution allows for two tri…

Fisher Matrix for Beginners
David Wittman (UC Davis)
https://arxiv.org/abs/2510.09683 https://arxiv.org/pdf/2510.09683

Fisher Matrix for Beginners
The Fisher information matrix is used widely in astronomy (and presumably other fields) to forecast the precision of future experiments while they are still in the design phase. Although many sources describe the mathematics of the formalism, few sources offer simple examples to help the beginner. This pedagogical document works through a few simple examples to develop conceptual understanding of the applications.

Matrix generators for Weil representations
Mikko Korhonen
https://arxiv.org/abs/2510.12261 https://arxiv.org/pdf/2510.12261

Matrix generators for Weil representations
Let $r$ be an odd prime and $\mathbb{F}$ a field containing a primitive $r$th root of unity. Then for all $\ell \geq 1$, there is a faithful representation $f: \operatorname{Sp}_{2\ell}(r) \rightarrow \operatorname{GL}_{r^\ell}(\mathbb{F})$ called the Weil representation. We provide explicit matrices generating $\operatorname{Sp}_{2\ell}(r)$ in $\operatorname{GL}_{r^\ell}(\mathbb{F})$, which we have implemented in Magma. We also describe such generators for the irreducible Weil representations …

Bound on entanglement in neural quantum states
Nisarga Paul
https://arxiv.org/abs/2510.11797 https://arxiv.org/pdf/2510.11797

Bound on entanglement in neural quantum states
Variational wavefunctions offer a practical route around the exponential complexity of many-body Hilbert spaces, but their expressive power is often sharply constrained. Matrix product states, for instance, are efficient but limited to area law entangled states. Neural quantum states (NQS) are widely believed to overcome such limitations, yet little is known about their fundamental constraints. Here we prove that feed-forward neural quantum states acting on $n$ spins with $k$ scalar nonlinearit…

Computing Safe Control Inputs using Discrete-Time Matrix Control Barrier Functions via Convex Optimization
James Usevitch, Juan Augusto Paredes Salazar, Ankit Goel
https://arxiv.org/abs/2510.09925

Computing Safe Control Inputs using Discrete-Time Matrix Control Barrier Functions via Convex Optimization
Control barrier functions (CBFs) have seen widespread success in providing forward invariance and safety guarantees for dynamical control systems. A crucial limitation of discrete-time formulations is that CBFs that are nonconcave in their argument require the solution of nonconvex optimization problems to compute safety-preserving control inputs, which inhibits real-time computation of control inputs guaranteeing forward invariance. This paper presents a novel method for computing safety-prese…

Reciprocal relations for orthogonal quantum matrices
Pavel Pyatov, Oleg Ogievetsky
https://arxiv.org/abs/2510.09811 https://arxiv.org/pdf/2510.09811…

Reciprocal relations for orthogonal quantum matrices
For the family of the orthogonal quantum matrix algebras we investigate the structure of their characteristic subalgebras -- special commutative subalgebras, which for the subfamily of the reflection equation algebras appear to be central. In [OP1] we described three generating sets of the characteristic subalgebras of the symplectic and orthogonal quantum matrix algebras. One of these -- the set of the elementary sums -- is finite. In the symplectic case the elementary sums are in general alge…

FedLoDrop: Federated LoRA with Dropout for Generalized LLM Fine-tuning
Sijing Xie, Dingzhu Wen, Changsheng You, Qimei Chen, Mehdi Bennis, Kaibin Huang
https://arxiv.org/abs/2510.12078

FedLoDrop: Federated LoRA with Dropout for Generalized LLM Fine-tuning
Fine-tuning (FT) large language models (LLMs) is crucial for adapting general-purpose models to specific tasks, enhancing accuracy and relevance with minimal resources. To further enhance generalization ability while reducing training costs, this paper proposes Federated LoRA with Dropout (FedLoDrop), a new framework that applies dropout to the rows and columns of the trainable matrix in Federated LoRA. A generalization error bound and convergence analysis under sparsity regularization are obta…

Sublinear Metric Steiner Forest via Maximal Independent Set
Sepideh Mahabadi, Mohammad Roghani, Jakub Tarnawski, Ali Vakilian
https://arxiv.org/abs/2510.11627 https://

Sublinear Metric Steiner Forest via Maximal Independent Set
In this work we consider the Metric Steiner Forest problem in the sublinear time model. Given a set $V$ of $n$ points in a metric space where distances are provided by means of query access to an $n\times n$ distance matrix, along with a set of $k$ terminal pairs $(s_1,t_1), \dots, (s_k,t_k)\in V\times V$, the goal is to find a minimum-weight subset of edges that connects each terminal pair. Although sublinear time algorithms have been studied for estimating the weight of a minimum spanning tre…

Gradient-flowed operator product expansion without IR renormalons
Martin Beneke (TU Munich), Hiromasa Takaura (Kyoto University)
https://arxiv.org/abs/2510.12193 https://…

Gradient-flowed operator product expansion without IR renormalons
A long-standing problem concerns the question how to consistently combine perturbative expansions in QCD with power corrections in the context of the operator product expansion (OPE), since the former exhibit ambiguities due to infrared renormalons, which are of the same order as the power corrections. We propose to use the gradient flow time $1/\sqrt{t}$ as a factorization scale and to express the OPE in terms of IR renormalon-free subtracted perturbative expansions and unambiguous matrix elem…

Global Convergence of Four-Layer Matrix Factorization under Random Initialization
Minrui Luo, Weihang Xu, Xiang Gao, Maryam Fazel, Simon Shaolei Du
https://arxiv.org/abs/2511.09925 https://arxiv.org/pdf/2511.09925 https://arxiv.org/html/2511.09925
arXiv:2511.09925v1 Announce Type: new
Abstract: Gradient descent dynamics on the deep matrix factorization problem is extensively studied as a simplified theoretical model for deep neural networks. Although the convergence theory for two-layer matrix factorization is well-established, no global convergence guarantee for general deep matrix factorization under random initialization has been established to date. To address this gap, we provide a polynomial-time global convergence guarantee for randomly initialized gradient descent on four-layer matrix factorization, given certain conditions on the target matrix and a standard balanced regularization term. Our analysis employs new techniques to show saddle-avoidance properties of gradient decent dynamics, and extends previous theories to characterize the change in eigenvalues of layer weights.
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Crosslisted article(s) found for astro-ph.CO. https://arxiv.org/list/astro-ph.CO/new
[1/1]:
- Fisher Matrix for Beginners
David Wittman (UC Davis)
https://

Abstract second-order boundary control systems
Till Preuster, Timo Reis, Manuel Schaller
https://arxiv.org/abs/2510.10363 https://arxiv.org/pdf/2510.10363

Abstract second-order boundary control systems
We consider abstract second order systems of the form $\ddot{x}(t) + D \dot{x}(t) + Sx(t)=0$, which are typically analyzed via the operator matrix $\mathcal{A}=\left[\begin{smallmatrix} 0 & I \\ -S & -D \end{smallmatrix}\right]$ governing the free dynamics of the corresponding first-order in time formulation. While previous work (e.g. on spectral properties of) $\mathcal{A}$ has focused on self-adjoint uniformly positive $S$, we consider the more general case which comprises the situation w…

Electron-electron scattering processes in quantum wells in a quantizing magnetic field: II. Scattering in the case of two subbands
M. P. Telenkov, Yu. A. Mityagin
https://arxiv.org/abs/2510.09787

Electron-electron scattering processes in quantum wells in a quantizing magnetic field: II. Scattering in the case of two subbands
Electron-electron scattering processes involving Landau levels of two subband are considered. A matrix of electron-electron scattering rates containing all tipes of transitions between Landau levels is calculated/ This matrix is analized, and the relative rates of transitions of different types are determined. The effect of the quantizing magnetic field orientation on electron-electron scattering processes is ectablished.

On weak convergence of Gaussian conditional distributions
Sarah Lumpp, Mathias Drton
https://arxiv.org/abs/2510.12412 https://arxiv.org/pdf/2510.12412

On weak convergence of Gaussian conditional distributions
Weak convergence of joint distributions generally does not imply convergence of conditional distributions. In particular, conditional distributions need not converge when joint Gaussian distributions converge to a singular Gaussian limit. Algebraically, this is due to the fact that at singular covariance matrices, Schur complements are not continuous functions of the matrix entries. Our results lay out special conditions under which convergence of Gaussian conditional distributions nevertheless…

Interaction Dynamics of Borrelia Surface Proteins with Fibronectin
Kavindi Madduma Hewage, Carlos Munoz, Mehmet Ozdogan, Catherine A. Brissette, Nuri Oncel
https://arxiv.org/abs/2510.11931

Interaction Dynamics of Borrelia Surface Proteins with Fibronectin
Lyme disease, caused by the bacterium Borrelia burgdorferi, is a significant public health concern in North America, with approximately 500,000 cases reported annually in the United States. The dissemination of B. burgdorferi from the initial tick bite site to various tissues is facilitated by surface adhesins that bind to extracellular matrix (ECM) proteins such as fibronectin (Fn). This study investigates the binding dynamics of B. burgdorferi surface proteins RevA, BBK32, BmpA, OspA, and Osp…

Conformal Data for the O(3) Wilson-Fisher CFT from Fuzzy Sphere Realization of Quantum Rotor Model
Arjun Dey, Loic Herviou, Christopher Mudry, Andreas Martin L\"auchli
https://arxiv.org/abs/2510.09755

Conformal Data for the O(3) Wilson-Fisher CFT from Fuzzy Sphere Realization of Quantum Rotor Model
We present a model for strongly interacting fermions with internal O(3) symmetry on the fuzzy-sphere that (i) preserves the rotational symmetry of the fuzzy sphere and (ii) undergoes a quantum phase transition in the (2+1)-dimensional O(3) Wilson-Fisher universality class. Using exact diagonalization (ED) and density matrix renormalization group (DMRG), we locate the quantum critical point via conformal perturbation theory and obtain scaling dimensions from finite-size spectra. We identify 24 p…

Triaxiality and shape dynamics in $^{70}$Ge
T. M. Kowalewski, A. D. Ayangeakaa, N. Sensharma, R. V. F. Janssens, Y. M. Wang, Q. B. Chen, J. M. Allmond, C. M. Campbell, S. Carmichael, M. P. Carpenter, P. Copp, C. Cousins, M. Devlin, U. Garg, C. M\"uller-Gatermann, T. J. Gray, D. J. Hartley, J. Heery, J. Henderson, H. Jayatissa, S. R. Johnson, S. P. Kisyov, F. G. Kondev, T. Lauritsen, S. Nandi, R. Rathod, W. Reviol, M. Rocchini, E. Rubino, R. Russell, A. Saracino, D. Seweryniak, M. …

Triaxiality and shape dynamics in $^{70}$Ge
The electromagnetic properties of low-lying states in $^{70}$Ge were investigated via multi-step Coulomb excitation of a $^{70}$Ge beam impinging on a $^{208}$Pb target at the ATLAS facility of the Argonne National Laboratory. A total of 27 transitional elements and six diagonal matrix elements coupling 11 low-lying states, were extracted from the measured cross sections. These were used to calculate reduced transition probabilities, spectroscopic quadrupole moments, and rotational invariant sh…

ECMSim: A high-performance web simulation of cardiac ECM remodeling through integrated ODE-based signaling and diffusion
Hasi Hays, William Richardson
https://arxiv.org/abs/2510.12577

ECMSim: A high-performance web simulation of cardiac ECM remodeling through integrated ODE-based signaling and diffusion
Extracellular matrix (ECM) remodeling is central to a wide variety of healthy and diseased tissue processes. Unfortunately, predicting ECM remodeling under various chemical and mechanical conditions has proven to be excessively challenging, due in part to its complex regulation by intracellular and extracellular molecular reaction networks that are spatially and temporally dynamic. We introduce ECMSim, which is a highly interactive, real-time, and web application designed to simulate heterogene…

Active Subspaces in Infinite Dimension
Poorbita Kundu, Nathan Wycoff
https://arxiv.org/abs/2510.11871 https://arxiv.org/pdf/2510.11871

Active Subspaces in Infinite Dimension
Active subspace analysis uses the leading eigenspace of the gradient's second moment to conduct supervised dimension reduction. In this article, we extend this methodology to real-valued functionals on Hilbert space. We define an operator which coincides with the active subspace matrix when applied to a Euclidean space. We show that many of the desirable properties of Active Subspace analysis extend directly to the infinite dimensional setting. We also propose a Monte Carlo procedure and discus…

Semiclassical analytical solutions of the eigenstate thermalization hypothesis in a quantum billiard
Yaoqi Ye, Chengkai Lin, Xiao Wang
https://arxiv.org/abs/2510.12517 https://

Semiclassical analytical solutions of the eigenstate thermalization hypothesis in a quantum billiard
We derive semiclassical analytical solutions for both the diagonal and off-diagonal functions in the eigenstate thermalization hypothesis (ETH) in a quarter-stadium quantum billiard. For a representative observable, we obtain an explicit expression and an asymptotic closed-form solution that naturally separate into a local contribution and a phase-space correlation term. These analytical results predict the band structure of the observable matrix, including its bandwidth and scaling behavior. W…

Spectral and Nilpotent Matrix Orderings: Comparison and Applications in Dynamic Systems
Shih-Yu Chang
https://arxiv.org/abs/2510.08579 https://arxiv.org/pd…

Spectral and Nilpotent Matrix Orderings: Comparison and Applications in Dynamic Systems
In our earlier work, we proposed the \emph{Spectral and Nilpotent Ordering} (SNO) as a new framework that extends matrix comparison beyond the Hermitian setting by incorporating both spectral and nilpotent structures. Building on that foundation, the present paper develops concrete certificates and applications of SNO. First, we employ generalized Gershgorin theorems to design certificates for spectral ordering that avoid direct eigenvalue computation and analyze their robustness under perturba…

Continuous Inverse Ambiguous Functions on Lie Groups
David Schmitz, Sadman Rahman, Anthony Kindness
https://arxiv.org/abs/2510.09958 https://arxiv.org/pdf/…

Continuous Inverse Ambiguous Functions on Lie Groups
In a previous study, the first author defines an inverse ambiguous function on a group $G$ to be a bijective function $f : G \to G$ satisfying the functional equation $f^{-1}(x) = f(x^{-1})$ for all $x \in G$. In this paper, we investigate the existence of continuous inverse ambiguous functions on classical Lie groups. In particular, we look at tori, elliptic curves over various fields, vector spaces, additive matrix groups, and multiplicative matrix groups.

Decomposing Conditional Independence Ideals with Hidden Variables: A Matroid-Theoretic Approach
Emiliano Liwski
https://arxiv.org/abs/2510.12570 https://ar…

Decomposing Conditional Independence Ideals with Hidden Variables: A Matroid-Theoretic Approach
We study a class of determinantal ideals arising from conditional independence (CI) statements with hidden variables. Such CI statements translate into determinantal conditions on a matrix whose entries represent the probabilities of events involving the observed random variables. Our main objective is to determine the irreducible components of the corresponding varieties and to provide a combinatorial or geometric interpretation of each. We achieve this by introducing a new approach rooted in …

Thinned COE random matrix models for DNA replication
Huw Day, Nina C. Snaith
https://arxiv.org/abs/2510.11748 https://arxiv.org/pdf/2510.11748

Thinned COE random matrix models for DNA replication
This paper details an observation that for more primitive organisms, such as some yeasts, the statistical distribution of the origins of replication sometimes looks remarkably like the distribution of eigenvalues from the Circular Orthogonal Ensemble (COE) of random matrices. This does not hold for more complex organisms, but a uniform thinning of the COE eigenvalues (which interpolates between the COE and uncorrelated, Poisson statistics) gives a platform to investigate characteristics of repl…

Audible Networks: Deconstructing and Manipulating Sounds with Deep Non-Negative Autoencoders
Juan Jos\'e Burred, Carmine-Emanuele Cella
https://arxiv.org/abs/2510.08816 http…

Audible Networks: Deconstructing and Manipulating Sounds with Deep Non-Negative Autoencoders
We propose the use of Non-Negative Autoencoders (NAEs) for sound deconstruction and user-guided manipulation of sounds for creative purposes. NAEs offer a versatile and scalable extension of traditional Non-Negative Matrix Factorization (NMF)-based approaches for interpretable audio decomposition. By enforcing non-negativity constraints through projected gradient descent, we obtain decompositions where internal weights and activations can be directly interpreted as spectral shapes and temporal …

Symmetry Analysis of Coupled Scalar Systems under Time Delay
Fatihcan M. Atay, Haibo Ruan
https://arxiv.org/abs/2510.11911 https://arxiv.org/pdf/2510.11911…

Symmetry Analysis of Coupled Scalar Systems under Time Delay
We study systems of coupled units in a general network configuration with a coupling delay. We show that the destabilizing bifurcations from an equilibrium are governed by the extreme eigenvalues of the coupling matrix of the network. Based on the equivariant degree method and its computational packages, we perform a symmetry classification of destabilizing bifurcations in bidirectional rings of coupled units. Both stationary and oscillatory bifurcations are discussed. We also introduce the con…

Beyond-Diagonal RIS Architecture Design and Optimization under Physics-Consistent Models
Zheyu Wu, Matteo Nerini, Bruno Clerckx
https://arxiv.org/abs/2510.12366 https://

Beyond-Diagonal RIS Architecture Design and Optimization under Physics-Consistent Models
Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. Conventional RIS is constrained to a diagonal scattering matrix, which limits its flexibility. Recently, beyond-diagonal RIS (BD-RIS) has been proposed as a more general RIS architecture class that allows inter-element connections and shows great potential for performance improvement. Despite extensive progress on BD-RIS, most existing studies rely on simplified channel models that igno…

Generalized Bishop frames of regular time-like curves in 4-dimensional Lorentz space $\mathbb{L}^{4}$
Subaru Nomoto
https://arxiv.org/abs/2510.09345 https://

Generalized Bishop frames of regular time-like curves in 4-dimensional Lorentz space $\mathbb{L}^{4}$
We introduced generalized Bishop frames on curves in 4-dimensional Euclidean space $\mathbb{E}^{4}$, which are orthonormal frames such that the derivatives of the vectors of the frames along the curve can be expressed, via a certain matrix, as a linear combination of the vectors of the frame. In relation to that, we study generalized Bishop frames of regular time-like curves. In a previous work, we showed that there is a hierarchy among different types of generalized Bishop frames for regular…

Trajectory control of a suspended load with non-stopping flying carriers
Sofia Girardello, Giulia Michieletto, Angelo Cenedese, Antonio Franchi, Chiara Gabellieri
https://arxiv.org/abs/2510.11413

Trajectory control of a suspended load with non-stopping flying carriers
This paper presents the first closed-loop control framework for cooperative payload transportation with non-stopping flying carriers. Building upon grasp-matrix formulations and internal force redundancy, we propose a feedback wrench controller that actively regulates the payload's pose while an optimization layer dynamically shapes internal-force oscillations to guarantee persistent carrier motion. Preliminary experimental results on multirotor UAVs validate the model assumptions, and numerica…

Forward and backward error bounds for a mixed precision preconditioned conjugate gradient algorithm
Thomas Bake, Erin Carson, Yuxin Ma
https://arxiv.org/abs/2510.11379 https://

Forward and backward error bounds for a mixed precision preconditioned conjugate gradient algorithm
The preconditioned conjugate gradient (PCG) algorithm is one of the most popular algorithms for solving large-scale linear systems Ax = b, where A is a symmetric positive definite matrix. Rather than computing residuals directly, it updates the residual vectors recursively. Current analyses of the conjugate gradient (CG) algorithm in finite precision typically assume that the norm of the recursively updated residual goes orders of magnitude below the machine precision, focusing mainly on boundi…

Replaced article(s) found for nlin.SI. https://arxiv.org/list/nlin.SI/new
[1/1]:
- The $r$-matrix structure on the moduli space of framed Higgs pairs
M. Bertola

Ly{\alpha} Intensity Mapping in HETDEX: Galaxy-Ly{\alpha} Intensity Cross-Power Spectrum
Maja Lujan Niemeyer, Eiichiro Komatsu, Jos\'e Luis Bernal, Chris Byrohl, Robin Ciardullo, Olivia Curtis, Daniel J. Farrow, Steven L. Finkelstein, Karl Gebhardt, Caryl Gronwall, Gary J. Hill, Matt J. Jarvis, Donghui Jeong, Erin Mentuch Cooper, Deeshani Mitra, Shiro Mukae, Julian B. Mu\~noz, Masami Ouchi, Shun Saito, Donald P. Schneider, Lutz Wisotzki

Lyα Intensity Mapping in HETDEX: Galaxy-Lyα Intensity Cross-Power Spectrum
We present a measurement of the Lyman-$α$ (Ly$α$) intensity mapping power spectrum from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). We measure the cross-power spectrum of the Ly$α$ intensity and Ly$α$-emitting galaxies (LAEs) in a redshift range of $1.9 < z < 3.5$. We calculate the intensity from HETDEX spectra that do not contain any detected LAEs above a signal-to-noise ratio of $5.5$. To produce a power spectrum model and its covariance matrix, we simulate the data using …

Slicing Is All You Need: Towards A Universal One-Sided Algorithm for Distributed Matrix Multiplication
Benjamin Brock, Renato Golin
https://arxiv.org/abs/2510.08874 https://

Slicing Is All You Need: Towards A Universal One-Sided Algorithm for Distributed Matrix Multiplication
Many important applications across science, data analytics, and AI workloads depend on distributed matrix multiplication. Prior work has developed a large array of algorithms suitable for different problem sizes and partitionings including 1D, 2D, 1.5D, and 2.5D algorithms. A limitation of current work is that existing algorithms are limited to a subset of partitionings. Multiple algorithm implementations are required to support the full space of possible partitionings. If no algorithm implemen…

On character tables for fusion systems
Thomas Lawrence, Jason Semeraro
https://arxiv.org/abs/2510.09277 https://arxiv.org/pdf/2510.09277

On character tables for fusion systems
A character table $X$ for a saturated fusion system $\mathcal{F}$ on a finite $p$-group $S$ is the square matrix of values associated to a basis of virtual $\mathcal{F}$-stable ordinary characters of $S$. We investigate a conjecture of the second author which equates the $p$-part of $|\det(X)|^2$ with the product of the orders of $S$-centralisers of fully $\mathcal{F}$-centralised $\mathcal{F}$-class representatives. This statement is exactly column orthogonality for the character tab…

Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation
Bogdan Butyrin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Qi-Man Shao, Zhuo-Song Zhang
https://arxiv.org/abs/2510.12375

Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation
In this paper, we refine the Berry-Esseen bounds for the multivariate normal approximation of Polyak-Ruppert averaged iterates arising from the linear stochastic approximation (LSA) algorithm with decreasing step size. We consider the normal approximation by the Gaussian distribution with covariance matrix predicted by the Polyak-Juditsky central limit theorem and establish the rate up to order $n^{-1/3}$ in convex distance, where $n$ is the number of samples used in the algorithm. We also prov…

Intelligent backpropagated neural networks application on Couette-Poiseuille flow of variable viscosity in a composite porous channel filled with an anisotropic porous layer
Timir Karmakar, Amrita Mandal
https://arxiv.org/abs/2510.08745

Intelligent backpropagated neural networks application on Couette-Poiseuille flow of variable viscosity in a composite porous channel filled with an anisotropic porous layer
This study examines Couette-Poiseuille flow of variable viscosity within a channel that is partially filled with a porous medium. To enhance its practical relevance, we assume that the porous medium is anisotropic with permeability varying in all directions, making it a positive semidefinite matrix in the momentum equation. We assume the Navier-Stokes equations govern the flow in the free flow region, while the Brinkman-Forchheimer-extended Darcy's equation governs the flow inside the porous me…

Formation of C-centers in Si-based systems by light ion irradiation
Carolina Crosta, Riccardo Nardin, Patrick Daoust, Stefano Achilli, Ian Colombo, Matteo Campostrini, Emiliano Bonera, Jacopo Pedrini, Oussama Moutanabbir, Valentino Rigato, Fabio Pezzoli
https://arxiv.org/abs/2510.12690

Formation of C-centers in Si-based systems by light ion irradiation
Atomic-scale crystal defects in Si are quantum-light sources offering tantalizing integration with existing photonic technologies. Yet, the controlled creation of near-infrared color centers for long- haul quantum communication and information still remains a challenge. In this work, we utilize light ions, such as H+ and He+, to gently generate quantum emitters in a crystalline Si matrix. Temperature-dependent photoluminescence measurements demonstrate the presence of optically-active defects, …

Crosslisted article(s) found for math.OA. https://arxiv.org/list/math.OA/new
[1/1]:
- Spectral and Nilpotent Matrix Orderings: Comparison and Applications in Dynamic Systems
Shih-Yu Chang

Variational Analysis in Spectral Decomposition Systems
H\`oa T. B\`ui, Minh N. B\`ui, Christian Clason
https://arxiv.org/abs/2510.11433 https://arxiv.org/p…

Variational Analysis in Spectral Decomposition Systems
This work is concerned with variational analysis of so-called spectral functions and spectral sets of matrices that only depend on eigenvalues of the matrix. Based on our previous work [H. T. Bùi, M. N. Bùi, and C. Clason, Convex analysis in spectral decomposition systems, arXiv 2503.14981] on convex analysis of such functions, we consider the question in the abstract framework of spectral decomposition systems, which covers a wide range of previously studied settings, including eigenvalue de…

Observation Matrix Design for Densifying MIMO Channel Estimation via 2D Ice Filling
Zijian Zhang, Mingyao Cui
https://arxiv.org/abs/2510.08887 https://arxi…

Observation Matrix Design for Densifying MIMO Channel Estimation via 2D Ice Filling
In recent years, densifying multiple-input multiple-output (MIMO) has attracted much attention from the communication community. Thanks to the subwavelength antenna spacing, the strong correlations among densifying antennas provide sufficient prior knowledge about channel state information (CSI). This inspires the careful design of observation matrices (e.g., transmit precoders and receive combiners), that exploits the CSI prior knowledge, to boost channel estimation performance. Aligned with t…

Efficient and accurate tensor network algorithm for Anderson impurity problems
Zhijie Sun, Zhenyu Li, Chu Guo
https://arxiv.org/abs/2510.11459 https://arxi…

Efficient and accurate tensor network algorithm for Anderson impurity problems
The Anderson impurity model (AIM) is of fundamental importance in condensed matter physics to study strongly correlated effects. However, accurately solving its long-time dynamics still remains a great numerical challenge. An emergent and rapidly developing numerical strategy to solve the AIM is to represent the Feynman-Vernon influence functional (IF), which encodes all the bath effects on the impurity dynamics, as a matrix product state (MPS) in the temporal domain. The computational cost of …

Intermediate chiral edge states in quantum Hall Josephson junctions
Partha Sarathi Banerjee, Rahul Marathe, Sankalpa Ghosh
https://arxiv.org/abs/2510.11432 https://

Intermediate chiral edge states in quantum Hall Josephson junctions
A transfer-matrix-based theoretical framework is developed to study transport in superconductor-quantum Hall-Superconductor (SQHS) Josephson junctions modulated by local potential barriers in the quantum-Hall regime. The method allows one to evaluate the change in the conductivity of such SQHS Josephson junctions contributed by the intermediate chiral edge states (ICES) induced by these local potential barriers at their electrostatic boundaries at specific electron filling-fractions. It is part…

SVD-based ugmt-gft on directed product graphs
Guoyun Xie, Zhichao Zhang
https://arxiv.org/abs/2510.10532 https://arxiv.org/pdf/2510.10532

SVD-based ugmt-gft on directed product graphs
Traditional directed graph signal processing generally depends on fixed representation matrices, whose rigid structures limit the model's ability to adapt to complex graph topologies. To address this issue, this study employed the unified graph representation matrix (UGRM) to propose a generalized graph Fourier transform (UGRM-GFT) method based on singular value decomposition (SVD) for signal analysis on directed graphs and Cartesian product graphs. We defined UGRM-GFT for general directed grap…

Presentation and uniqueness of Kac-Moody groups over local rings
Timoth\'ee Marquis, Bernhard M\"uhlherr
https://arxiv.org/abs/2510.11272 https://…

Presentation and uniqueness of Kac-Moody groups over local rings
To any generalised Cartan matrix (GCM) $A$ and any ring $R$, Tits associated a Kac-Moody group $\mathfrak{G}_A(R)$ defined by a presentation à la Steinberg. For a domain $R$ with field of fractions $\mathbb{K}$, we explore the question of whether the canonical map $φ_R\colon\thinspace \mathfrak{G}_A(R)\to \mathfrak{G}_A(\mathbb{K})$ is injective. This question for Cartan matrices has a long history, and for GCMs was already present in Tits' foundational papers on Kac-Moody groups. We prove th…

Crosslisted article(s) found for math.SP. https://arxiv.org/list/math.SP/new
[1/1]:
- Spectral and Nilpotent Matrix Orderings: Comparison and Applications in Dynamic Systems
Shih-Yu Chang

MIRANDA: short signatures from a leakage-free full-domain-hash scheme
Alain Couvreur, Thomas Debris-Alazard, Philippe Gaborit, Adrien Vin\c{c}otte
https://arxiv.org/abs/2510.07479

MIRANDA: short signatures from a leakage-free full-domain-hash scheme
We present $\mathsf{Miranda}$, the first family of full-domain-hash signatures based on matrix codes. This signature scheme fulfils the paradigm of Gentry, Peikert and Vaikuntanathan ($\mathsf{GPV}$), which gives strong security guarantees. Our trapdoor is very simple and generic: if we propose it with matrix codes, it can actually be instantiated in many other ways since it only involves a subcode of a decodable code (or lattice) in a unique decoding regime of parameters. Though $\mathsf{Miran…

Quantum Deception: Honey-X Deception using Quantum Games
Efstratios Reppas, Ali Wadi, Brendan Gould, Kyriakos G. Vamvoudakis
https://arxiv.org/abs/2510.11848 https://

Quantum Deception: Honey-X Deception using Quantum Games
In this paper, we develop a framework for deception in quantum games, extending the Honey-X paradigm from classical zero-sum settings into the quantum domain. Building on a view of deception in classical games as manipulation of a player's perception of the payoff matrix, we formalize quantum deception as controlled perturbations of the payoff Hamiltonian subject to a deception budget. We show that when victims are aware of possible deception, their equilibrium strategies surprisingly coincide …

AWM: Accurate Weight-Matrix Fingerprint for Large Language Models
Boyi Zeng, Lin Chen, Ziwei He, Xinbing Wang, Zhouhan Lin
https://arxiv.org/abs/2510.06738 https://

AWM: Accurate Weight-Matrix Fingerprint for Large Language Models
Protecting the intellectual property of large language models (LLMs) is crucial, given the substantial resources required for their training. Consequently, there is an urgent need for both model owners and third parties to determine whether a suspect LLM is trained from scratch or derived from an existing base model. However, the intensive post-training processes that models typically undergo-such as supervised fine-tuning, extensive continued pretraining, reinforcement learning, multi-modal ex…

Einstein and Debye temperatures, electron-phonon coupling constant and a probable mechanism for ambient-pressure room-temperature superconductivity in intercalated graphite
E. F. Talantsev
https://arxiv.org/abs/2511.07460 https://arxiv.org/pdf/2511.07460 https://arxiv.org/html/2511.07460
arXiv:2511.07460v1 Announce Type: new
Abstract: Recently, Ksenofontov et al (arXiv:2510.03256) observed ambient pressure room-temperature superconductivity in graphite intercalated with lithium-based alloys with transition temperature (according to magnetization measurements) $T_c=330$ $K$. Here, I analyzed the reported temperature dependent resistivity data $\rho(T)$ in these graphite-intercalated samples and found that $\rho(T)$ is well described by the model of two series resistors, where each resistor is described as either an Einstein conductor or a Bloch-Gr\"uneisen conductor. Deduced Einstein and Debye temperatures are $\Theta_{E,1} \approx 250$ $K$ and $\Theta_{E,2} \approx 1,600$ $K$, and $\Theta_{D,1} \approx 300$ $K$ and $\Theta_{D,2} \approx 2,200$ $K$, respectively. Following the McMillan formalism, from the deduced $\Theta_{E,2}$ and $\Theta_{D,2}$, the electron-phonon coupling constant $\lambda_{e-ph} = 2.2 - 2.6$ was obtained. This value of $\lambda_{e-ph}$ is approximately equal to the value of $\lambda_{e-ph}$ in highly compressed superconducting hydrides. Based on this, I can propose that the observed room-temperature superconductivity in intercalated graphite is localized in nanoscale Sr-Ca-Li metallic flakes/particles, which adopt the phonon spectrum from the surrounding bulk graphite matrix, and as a result, conventional electron-phonon superconductivity arises in these nano-flakes/particles at room temperature. Experimental data reported by Ksenofontov et al (arXiv:2510.03256) on trapped magnetic flux decay in intercalated graphite samples supports the proposition.
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Construction of Basis Functions for the Geometry Conforming Immersed Finite Element Method
Slimane Adjerid, Tao Lin, Haroun Meghaichi
https://arxiv.org/abs/2510.12018 https://…

Construction of Basis Functions for the Geometry Conforming Immersed Finite Element Method
The Frenet apparatus is a new framework for constructing high order geometry-conforming immersed finite element functions for interface problems. In this report, we present a procedure for constructing the local IFE bases in some detail as well as a new approach for constructing orthonormal bases using the singular value decomposition of the local generalized Vandermonde matrix. A sample implementation in MATLAB is provided to showcase the simplicity and extensionability of the framework.

Zero-Inflated Bayesian Multi-Study Infinite Non-Negative Matrix Factorization
Blake Hansen, Dafne Zorzetto, Valeria Edefonti, Roberta De Vito
https://arxiv.org/abs/2510.07518 ht…

Zero-Inflated Bayesian Multi-Study Infinite Non-Negative Matrix Factorization
Understanding the association between dietary patterns and health outcomes, such as the cancer risk, is crucial to inform public health guidelines and shaping future dietary interventions. However, dietary intake data present several statistical challenges: they are high-dimensional, often sparse with excess zeros, and exhibit heterogeneity driven by individual-level covariates. Non-Negative Matrix Factorization (NMF), commonly used to estimate patterns in high-dimensional count data, typically…

Games in the matrix
Ilijas Farah
https://arxiv.org/abs/2511.07456 https://arxiv.org/pdf/2511.07456

Electron-phonon coupling in magnetic materials using the local spin density approximation
\'A. A. Carrasco \'Alvarez, M. Giantomassi, J. Lihm, G. E. Allemand, M. Mignolet, M. Verstraete, S. Ponc\'e
https://arxiv.org/abs/2510.11350

Electron-phonon coupling in magnetic materials using the local spin density approximation
Magnetic materials are crucial for manipulating electron spin and magnetic fields, enabling applications in data storage, spintronics, charge transport, and energy conversion, while also providing insight into fundamental quantum phenomena. In numerous applications, the interaction between electrons and lattice vibrations, known as electron-phonon coupling, can be of significant importance. In that regard, we extend the EPW package to be able to interpolate the electron-phonon matrix elements c…

Robust Least Squares Problems with Binary Uncertain Data
Yang Zhou, Xiaojun Chen
https://arxiv.org/abs/2510.11519 https://arxiv.org/pdf/2510.11519

Robust Least Squares Problems with Binary Uncertain Data
We propose a Binary Robust Least Squares (BRLS) model that encompasses key robust least squares formulations, such as those involving uncertain binary labels and adversarial noise constrained within a hypercube. We show that the geometric structure of the noise propagation matrix, particularly whether its columns form acute or obtuse angles, implies the supermodularity or submodularity of the inner maximization problem. This structural property enables us to integrate powerful combinatorial opt…

FLEET: Formal Language-Grounded Scheduling for Heterogeneous Robot Teams
Corban Rivera, Grayson Byrd, Meghan Booker, Bethany Kemp, Allison Gaines, Emma Holmes, James Uplinger, Celso M de Melo, David Handelman
https://arxiv.org/abs/2510.07417

FLEET: Formal Language-Grounded Scheduling for Heterogeneous Robot Teams
Coordinating heterogeneous robot teams from free-form natural-language instructions is hard. Language-only planners struggle with long-horizon coordination and hallucination, while purely formal methods require closed-world models. We present FLEET, a hybrid decentralized framework that turns language into optimized multi-robot schedules. An LLM front-end produces (i) a task graph with durations and precedence and (ii) a capability-aware robot--task fitness matrix; a formal back-end solves a ma…

MSHT Approximate N3LO PDFs: Updates and Consequences for Phenomenology
T. Cridge, L. A. Harland-Lang, R. S. Thorne
https://arxiv.org/abs/2510.09321 https://

MSHT Approximate N3LO PDFs: Updates and Consequences for Phenomenology
We present updates to the MSHT approximate N3LO PDFs focusing upon recent developments, examining the impacts of newly determined splitting function and transition matrix element calculations, performed since the public MSHT20aN3LO PDF set was released. We observe only small changes to the output PDFs, at most of similar size to their quoted uncertainties and typically notably less. Finally, we also present the impacts on the PDF luminosities, where changes are generally further reduced.

Privacy-Preserving Distributed Estimation with Limited Data Rate
Jieming Ke, Jimin Wang, Ji-Feng Zhang
https://arxiv.org/abs/2510.12549 https://arxiv.org/p…

Privacy-Preserving Distributed Estimation with Limited Data Rate
This paper focuses on the privacy-preserving distributed estimation problem with a limited data rate, where the observations are the sensitive information. Specifically, a binary-valued quantizer-based privacy-preserving distributed estimation algorithm is developed, which improves the algorithm's privacy-preserving capability and simultaneously reduces the communication costs. The algorithm's privacy-preserving capability, measured by the Fisher information matrix, is dynamically enhanced over…

Entanglement and accidental symmetries in the nucleon-nucleon system
Alma L. Cavallin, Oliver Thim, Christian Forss\'en
https://arxiv.org/abs/2510.09466 https://

Entanglement and accidental symmetries in the nucleon-nucleon system
We study the connection between accidental symmetries in the nuclear interaction and spin entanglement in two-nucleon scattering. Specifically, we incorporate different levels of Wigner $SU(4)$ and Serber symmetries into leading-order potentials derived from chiral effective field theory. We conduct a quantitative analysis by computing the full $S$ matrix, demonstrating that the neutron-proton spin entanglement can be related to the symmetry properties of the interaction and the presence of cer…

A simpler kernel for stratified Mukai flops
Ed Segal, Wei Tseu
https://arxiv.org/abs/2510.08250 https://arxiv.org/pdf/2510.08250

A simpler kernel for stratified Mukai flops
We reinvestigate the problem of describing the Fourier-Mukai kernel for the derived equivalence associated to a stratified Mukai flop. For the case of Grassmannians of planes we give a very simple geometric construction of the kernel, using the framework of matrix factorizations.

Fiber-optic power limiter device based on carbon nanotubes
Ekaterina Borisova, Anastasiya Ponosova, Natalia Arutyunyan, Alexey Shilko, Elena Obraztsova, Boris Galagan, Vadim Makarov
https://arxiv.org/abs/2510.09301

Fiber-optic power limiter device based on carbon nanotubes
We experimentally demonstrate a power limiter based on single-walled carbon nanotubes dispersed in a polymer matrix. This simple fiber-optic device permanently increases its attenuation when subjected to 50-mW or higher cw illumination at 1550 nm and initiates a fiber-fuse effect at 1 to 5 W. It may be used for protecting quantum key distribution equipment from light-injection attacks. We demonstrate its compatibility with phase- and polarisation-encoding quantum key distribution systems.

MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning
Tajamul Ashraf, Umair Nawaz, Abdelrahman M. Shaker, Rao Anwer, Philip Torr, Fahad Shahbaz Khan, Salman Khan
https://arxiv.org/abs/2510.08567

MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning
Vision language models (VLMs) are increasingly deployed as controllers with access to external tools for complex reasoning and decision-making, yet their effectiveness remains limited by the scarcity of high-quality multimodal trajectories and the cost of manual annotation. We address this challenge with a vision-centric agent tuning framework that automatically synthesizes multimodal trajectories, generates step-wise preference pairs, and trains a VLM controller for robust tool-use reasoning. …

\'Etude de quelques familles de $\lambda$-quiddit\'es et minoration de la taille maximale des $\lambda$-quiddit\'es irr\'eductibles sur un corps fini
Flavien Mabilat
https://arxiv.org/abs/2510.09219

Étude de quelques familles de $λ$-quiddités et minoration de la taille maximale des $λ$-quiddités irréductibles sur un corps fini
$λ$-quiddities of size $n$ are $n$-tuples of elements from a fixed set that are solutions to a matrix equation which is fundamental in the study of the combinatorics of the modular group and Coxeter's friezes. To gain further insight into these objects, we use a notion of irreducibility, which allows restricting the study to a limited number of elements that must be determined for each set. Our goal here is to define several families of $λ$-quiddities over finite fields and to study their irr…

A Note on Idempotent Matrices: The Poset Structure and The Construction
Sen-Peng Eu, Yong-Siang Lin, Wei-Liang Sun
https://arxiv.org/abs/2510.09501 https://

A Note on Idempotent Matrices: The Poset Structure and The Construction
Idempotent elements play a fundamental role in ring theory, as they encode significant information about the underlying algebraic structure. In this paper, we study idempotent matrices from two perspectives. First, we analyze the partially ordered set of idempotents in matrix rings over a division ring. We characterize the partial order relation explicitly in terms of block decompositions of idempotent matrices. Second, over principal ideal domains, we establish an equivalent condition for a ma…

On a matrix constrained CKP hierarchy
Song Li, Kelei Tian, Zhiwei Wu
https://arxiv.org/abs/2510.09054 https://arxiv.org/pdf/2510.09054

On a matrix constrained CKP hierarchy
The algebraic structures of integrable hierarchies play an important role in the study of soliton equations. In this paper, we use splitting theory to give a matrix representation of a constrained CKP hierarchy, which can be considered as a generalization of the $\hat{A}_{2n}^{(2)}$-KdV hierarchy and the constrained KP hierarchy. An equivalent construction in terms of the pseudo-differential operator is discussed. Darboux transformations, scaling transformation and tau functions $\ln τ_f$ for …

Replaced article(s) found for math.SP. https://arxiv.org/list/math.SP/new
[1/1]:
- The Hoffman-Wielandt inequality for quaternion matrices and quaternion matrix polynomials
Pallavi Basavaraju, Shrinath Hadimani, Sachindranath Jayaraman

Open heavy-flavor transport and hadronization in heavy-ion collisions
Yu Fu, Tharun Krishna, Weiyao Ke, Steffen A. Bass, Ralf Rapp
https://arxiv.org/abs/2510.08923 https://

Open heavy-flavor transport and hadronization in heavy-ion collisions
We develop a comprehensive model for heavy-quark evolution in a realistic QGP, from their production in the initial collision to hadronic freeze-out. Heavy-quark transport is described by a Langevin approach including medium-induced radiation, coupled to a 2+1D viscous hydrodynamic bulk evolution. Transport coefficients are obtained from non-perturbative $T$-matrix calculations with resonant correlations near the transition temperature. Hadronization is implemented via two fragmentation+recombi…

ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix Factorization
Lawrence Liu, Alexander Liu, Mengdi Wang, Tuo Zhao, Lin F. Yang
https://arxiv.org/abs/2510.05528

ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix Factorization
Large language models (LLMs) present significant deployment challenges due to their immense computational and memory requirements. While semi-structured pruning, particularly 2:4 sparsity, offers a path to practical hardware acceleration, existing methods often incur substantial performance degradation. To bridge this gap, we introduce ARMOR: (Adaptive Representation with Matrix-factORization), a novel one-shot post-training pruning algorithm. Instead of directly pruning weights, ARMOR factoriz…

Bootstrapping Yang-Mills matrix integrals
Wenliang Li, Xinran Su
https://arxiv.org/abs/2510.06704 https://arxiv.org/pdf/2510.06704

Bootstrapping Yang-Mills matrix integrals
We revisit the large $N$ limit of bosonic $D$-matrix Yang-Mills integrals using two complementary bootstrap methods. In the positivity bootstrap, we obtain bounds for $\langle \text{tr}\, XX \rangle$ and $\langle \text{tr}\, XXXX \rangle$ at various length cutoff $L_{\max}$. For $D=3$, we do not find an isolated region until $L_{\max}=12$. For larger $D$, the allowed regions become islands at $L_{\max}=8$ and shrink rapidly as $L_{\max}$ increases. The precision of some $L_{\max}=12$ islands is…

MATRIX: Mask Track Alignment for Interaction-aware Video Generation
Siyoon Jin, Seongchan Kim, Dahyun Chung, Jaeho Lee, Hyunwook Choi, Jisu Nam, Jiyoung Kim, Seungryong Kim
https://arxiv.org/abs/2510.07310

MATRIX: Mask Track Alignment for Interaction-aware Video Generation
Video DiTs have advanced video generation, yet they still struggle to model multi-instance or subject-object interactions. This raises a key question: How do these models internally represent interactions? To answer this, we curate MATRIX-11K, a video dataset with interaction-aware captions and multi-instance mask tracks. Using this dataset, we conduct a systematic analysis that formalizes two perspectives of video DiTs: semantic grounding, via video-to-text attention, which evaluates whether n…

Even Faster Kernel Matrix Linear Algebra via Density Estimation
Rikhav Shah, Sandeep Silwal, Haike Xu
https://arxiv.org/abs/2510.02540 https://arxiv.org/pd…

Even Faster Kernel Matrix Linear Algebra via Density Estimation
This paper studies the use of kernel density estimation (KDE) for linear algebraic tasks involving the kernel matrix of a collection of $n$ data points in $\mathbb R^d$. In particular, we improve upon existing algorithms for computing the following up to $(1+\varepsilon)$ relative error: matrix-vector products, matrix-matrix products, the spectral norm, and sum of all entries. The runtimes of our algorithms depend on the dimension $d$, the number of points $n$, and the target error $\varepsilon…

Investigating Matrix Repartitioning to Address the Over- and Undersubscription Challenge for a GPU-based CFD Solver
Gregor Olenik, Marcel Koch, Hartwig Anzt
https://arxiv.org/abs/2510.08536

Investigating Matrix Repartitioning to Address the Over- and Undersubscription Challenge for a GPU-based CFD Solver
Modern high-performance computing (HPC) increasingly relies on GPUs, but integrating GPU acceleration into complex scientific frameworks like OpenFOAM remains a challenge. Existing approaches either fully refactor the codebase or use plugin-based GPU solvers, each facing trade-offs between performance and development effort. In this work, we address the limitations of plugin-based GPU acceleration in OpenFOAM by proposing a repartitioning strategy that better balances CPU matrix assembly and GP…

Spectral analysis of large dimensional Chatterjee's rank correlation matrix
Zhaorui Dong, Fang Han, Jianfeng Yao
https://arxiv.org/abs/2510.07262 https://

Spectral analysis of large dimensional Chatterjee's rank correlation matrix
This paper studies the spectral behavior of large dimensional Chatterjee's rank correlation matrix when observations are independent draws from a high-dimensional random vector with independent continuous components. We show that the empirical spectral distribution of its symmetrized version converges to the semicircle law, and thus providing the first example of a large correlation matrix deviating from the Marchenko-Pastur law that governs those of Pearson, Kendall, and Spearman. We further e…

Computing moment polytopes - with a focus on tensors, entanglement and matrix multiplication
Maxim van den Berg, Matthias Christandl, Vladimir Lysikov, Harold Nieuwboer, Michael Walter, Jeroen Zuiddam
https://arxiv.org/abs/2510.08336

Computing moment polytopes - with a focus on tensors, entanglement and matrix multiplication
Tensors are fundamental in mathematics, computer science, and physics. Their study through algebraic geometry and representation theory has proved very fruitful in the context of algebraic complexity theory and quantum information. In particular, moment polytopes have been understood to play a key role. In quantum information, moment polytopes (also known as entanglement polytopes) provide a framework for the single-particle quantum marginal problem and offer a geometric characterization of ent…

Randomized and quantum approximate matrix multiplication
Simon Apers, Arjan Cornelissen, Samson Wang
https://arxiv.org/abs/2510.08509 https://arxiv.org/pdf…

Randomized and quantum approximate matrix multiplication
The complexity of matrix multiplication is a central topic in computer science. While the focus has traditionally been on exact algorithms, a long line of literature also considers randomized algorithms, which return an approximate solution in faster time. In this work, we adopt a unifying perspective that frames these randomized algorithms in terms of mean estimation. Using it, we first give refined analyses of classical algorithms based on random walks by Cohen-Lewis (`99), and based on sketc…

Near-optimal Rank Adaptive Inference of High Dimensional Matrices
Fr\'ed\'eric Zheng, Yassir Jedra, Alexandre Proutiere
https://arxiv.org/abs/2510.08117 https://

Near-optimal Rank Adaptive Inference of High Dimensional Matrices
We address the problem of estimating a high-dimensional matrix from linear measurements, with a focus on designing optimal rank-adaptive algorithms. These algorithms infer the matrix by estimating its singular values and the corresponding singular vectors up to an effective rank, adaptively determined based on the data. We establish instance-specific lower bounds for the sample complexity of such algorithms, uncovering fundamental trade-offs in selecting the effective rank: balancing the precis…

A Wigner Matrix Based Convolution Algorithm For Matrix Elements in the LCAO Method
Tyler C. Sterling
https://arxiv.org/abs/2510.06285 https://arxiv.org/pdf…

A Wigner Matrix Based Convolution Algorithm For Matrix Elements in the LCAO Method
The linear combination of atomic orbitals (LCAO) method uses a small basis set in exchange for expensive matrix element calculations. The most efficient approximation for the matrix element calculations is the two-center approximation (2CA) in tight binding (TB). In the 2CA, a variety of matrix elements are neglected with only "two-center integrals" (2CI) remaining. The 2CI are calculated efficiently by rotating to symmetrical coordinates where the integral is parameterized. This makes TB fast …

Gaussian Equivalence for Self-Attention: Asymptotic Spectral Analysis of Attention Matrix
Tomohiro Hayase, Beno\^it Collins, Ryo Karakida
https://arxiv.org/abs/2510.06685 https:…

Gaussian Equivalence for Self-Attention: Asymptotic Spectral Analysis of Attention Matrix
Self-attention layers have become fundamental building blocks of modern deep neural networks, yet their theoretical understanding remains limited, particularly from the perspective of random matrix theory. In this work, we provide a rigorous analysis of the singular value spectrum of the attention matrix and establish the first Gaussian equivalence result for attention. In a natural regime where the inverse temperature remains of constant order, we show that the singular value distribution of t…

Fisher Information, Training and Bias in Fourier Regression Models
Lorenzo Pastori, Veronika Eyring, Mierk Schwabe
https://arxiv.org/abs/2510.06945 https://

Fisher Information, Training and Bias in Fourier Regression Models
Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher information matrix (FIM) are effective for predicting their training and prediction performance. We exploit the equivalence between a broad class of QNNs and Fourier models, and study the interplay between the \emph{effective dimension} and the \emph{bias} of a model towards a given task, investigating how these affe…

Localized states of BFSS super quantum mechanics
Oscar J. C. Dias, Jorge E. Santos
https://arxiv.org/abs/2510.07379 https://arxiv.org/pdf/2510.07379…

Localized states of BFSS super quantum mechanics
We analyze the recently discovered localized and non-uniform phases of the Banks-Fischler-Shenker-Susskind (BFSS) matrix quantum mechanics. Building on [1], we provide first-principles derivations of their properties and extend the results with new analytic and numerical insights. We show that strongly coupled BFSS dynamics emerge from a specific Carrollian transformation of 11-dimensional supergravity, which we justify in detail. In this framework, the uniform BFSS phase corresponds to a black…

SliceFine: The Universal Winning-Slice Hypothesis for Pretrained Networks
Md Kowsher, Ali O. Polat, Ehsan Mohammady Ardehaly, Mehrdad Salehi, Zia Ghiasi, Prasanth Murali, Chen Chen
https://arxiv.org/abs/2510.08513

SliceFine: The Universal Winning-Slice Hypothesis for Pretrained Networks
This paper presents a theoretical framework explaining why fine tuning small, randomly selected subnetworks (slices) within pre trained models can be sufficient for downstream adaptation. We prove that pretrained networks exhibit a universal winning slice property arising from two phenomena: (1) spectral balance the eigenspectra of different weight matrix slices are remarkably similar; and (2) high task energy their backbone representations retain rich, task relevant features. This leads to the…

Preparation of initial states with open and periodic boundary conditions on quantum devices using matrix product states
Yibin Guo, Manuel Schneider, Takis Angelides, Karl Jansen, C. -J. David Lin, Yao Ting Su
https://arxiv.org/abs/2510.07125

Preparation of initial states with open and periodic boundary conditions on quantum devices using matrix product states
We present a framework for preparing quantum states from matrix product states (MPS) with open and periodic boundary conditions on quantum devices. The MPS tensors are mapped to unitary gates, which are subsequently decomposed into native gates on quantum hardware. States with periodic boundary conditions (pbc) can be represented efficiently as quantum circuits using ancilla qubits and post-selection after measurement. We derive an exact expression for the success rate of this probabilistic app…

Exploring the Spectral Edge in SYK Models
Bowen Ouyang, Pratik Rath
https://arxiv.org/abs/2510.07804 https://arxiv.org/pdf/2510.07804

Exploring the Spectral Edge in SYK Models
Previous work on Jackiw-Teitelboim (JT) gravity has shown that, at low temperatures, the annealed entropy becomes negative and departs from the quenched entropy. From the perspective of the random-matrix theory (RMT) dual of JT gravity, this effect is encoded in the level spacing statistics of the spectral edge that is universally described by the Airy model. At low temperature, the quenched entropy exhibits a power law dependence determined by the symmetry class of the RMT ensemble. Here we st…

Approximating quantum states by states of low rank
Nathaniel Johnston, Chi-Kwong Li
https://arxiv.org/abs/2510.08463 https://arxiv.org/pdf/2510.08463

Approximating quantum states by states of low rank
Given a positive integer k, it is natural to ask for a formula for the distance between a given density matrix (i.e., mixed quantum state) and the set of density matrices of rank at most k. This problem has already been solved when "distance" is measured in the trace or Frobenius norm. We solve it for all other unitary similarity invariant norms. We also present some consequences of our formula. For example, in the trace and Frobenius norms, the density matrix that is farthest from the set of l…

Permutation-Invariant Spectral Learning via Dyson Diffusion
Tassilo Schwarz, Cai Dieball, Constantin Kogler, Kevin Lam, Renaud Lambiotte, Arnaud Doucet, Alja\v{z} Godec, George Deligiannidis
https://arxiv.org/abs/2510.08535

Permutation-Invariant Spectral Learning via Dyson Diffusion
Diffusion models are central to generative modeling and have been adapted to graphs by diffusing adjacency matrix representations. The challenge of having up to $n!$ such representations for graphs with $n$ nodes is only partially mitigated by using permutation-equivariant learning architectures. Despite their computational efficiency, existing graph diffusion models struggle to distinguish certain graph families, unless graph data are augmented with ad hoc features. This shortcoming stems from…

Inverse Continuous-Time Linear Quadratic Regulator: From Control Cost Matrix to Entire Cost Reconstruction
Yuexin Cao, Yibei Li, Zhuo Zou, Xiaoming Hu
https://arxiv.org/abs/2510.04083

Inverse Continuous-Time Linear Quadratic Regulator: From Control Cost Matrix to Entire Cost Reconstruction
This paper studies the inverse optimal control problem for continuous-time linear quadratic regulators over finite-time horizon, aiming to reconstruct the control, state, and terminal cost matrices in the objective function from observed optimal inputs. Previous studies have mainly explored the recovery of state cost matrices under the assumptions that the system is controllable and the control cost matrix is given. Motivated by various applications in which the control cost matrix is unknown a…

A Probabilistic Basis for Low-Rank Matrix Learning
Simon Segert, Nathan Wycoff
https://arxiv.org/abs/2510.05447 https://arxiv.org/pdf/2510.05447

A Probabilistic Basis for Low-Rank Matrix Learning
Low rank inference on matrices is widely conducted by optimizing a cost function augmented with a penalty proportional to the nuclear norm $\Vert \cdot \Vert_*$. However, despite the assortment of computational methods for such problems, there is a surprising lack of understanding of the underlying probability distributions being referred to. In this article, we study the distribution with density $f(X)\propto e^{-λ\Vert X\Vert_*}$, finding many of its fundamental attributes to be analytically…

An efficient algorithm for kernel quantile regression
Shengxiang Deng, Xudong Li, Yangjing Zhang
https://arxiv.org/abs/2510.07929 https://arxiv.org/pdf/251…

An efficient algorithm for kernel quantile regression
Kernel Quantile Regression (KQR) extends classical quantile regression to nonlinear settings using kernel methods, offering powerful tools for modeling conditional distributions. However, its application to large-scale datasets is severely limited by the computational burden of the large, dense kernel matrix and the need to efficiently solve large, often ill-conditioned linear systems. Existing state-of-the-art solvers usually struggle with scalability. In this paper, we propose a novel and hig…