Tootfinder

Instance-Optimal Matrix Multiplicative Weight Update and Its Quantum Applications
Weiyuan Gong, Tongyang Li, Xinzhao Wang, Zhiyu Zhang
https://arxiv.org/abs/2509.08911 https://

Instance-Optimal Matrix Multiplicative Weight Update and Its Quantum Applications
The Matrix Multiplicative Weight Update (MMWU) is a seminal online learning algorithm with numerous applications. Applied to the matrix version of the Learning from Expert Advice (LEA) problem on the $d$-dimensional spectraplex, it is well known that MMWU achieves the minimax-optimal regret bound of $O(\sqrt{T\log d})$, where $T$ is the time horizon. In this paper, we present an improved algorithm achieving the instance-optimal regret bound of $O(\sqrt{T\cdot S(X||d^{-1}I_d)})$, where…

Gaussian Copula-Based Outage Performance Analysis of Fluid Antenna Systems: Channel Coefficient- or Envelope-Level Correlation Matrix?
Rui Xu, Yinghui Ye, Xiaoli Chu, Guangyue Lu, Farshad Rostami Ghadi, Kai-Kit Wong
https://arxiv.org/abs/2509.09411

Gaussian Copula-Based Outage Performance Analysis of Fluid Antenna Systems: Channel Coefficient- or Envelope-Level Correlation Matrix?
Gaussian copula has been employed to evaluate the outage performance of Fluid Antenna Systems (FAS), with the covariance matrix reflecting the dependence among multivariate normal random variables (RVs). While prior studies approximate this matrix using the channel coefficient correlation matrix from Jake's model, this work instead employs the channel envelope correlation matrix, motivated by the fact that the multivariate normal RVs are generated by transforming correlated channel envelopes. T…

Work statistics of sudden Quantum quenches: A random matrix theory perspective on Gaussianity and its deviations
Miguel Tierz
https://arxiv.org/abs/2509.09640 https://

Work statistics of sudden Quantum quenches: A random matrix theory perspective on Gaussianity and its deviations
We show that, for sudden quenches, the work distribution reduces to the statistics of traces of powers of Haar unitaries, which are random unitary matrices drawn uniformly from the unitary group. For translation-invariant quadratic fermionic chains with interactions extending to $m$ neighbors and periodic boundary conditions, the Loschmidt amplitude admits a unitary matrix-model / Toeplitz representation, which yields a work variable of the form $W=\sum_{r\le m} a_r\,\mathrm{Re}\,\mathrm{Tr}\,U…

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…

Boolean Rank via Monomial Ideals
Juliann Geraci, Alexander B. Kunin, Alexandra Seceleanu
https://arxiv.org/abs/2509.09570 https://arxiv.org/pdf/2509.09570

Boolean Rank via Monomial Ideals
Boolean matrix factorization (BMF) has many applications in data mining, bioinformatics, and network analysis. The goal of BMF is to decompose a given binary matrix as the Boolean product of two smaller binary matrices, revealing underlying structure in the data. When interpreting a binary matrix as the adjacency matrix of a bipartite graph, BMF is equivalent to the NP-hard biclique cover problem. By approaching this problem through the lens of commutative algebra, we utilize algebraic struct…

Matrix-valued bispectral discrete orthogonal polynomials
I. Bono Parisi
https://arxiv.org/abs/2509.08909 https://arxiv.org/pdf/2509.08909

Matrix-valued bispectral discrete orthogonal polynomials
We develop a unified construction of matrix-valued orthogonal polynomials associated with discrete weights, yielding bispectral sequences as eigenfunctions of second-order difference operators. This general framework extends the discrete families in the classical Askey scheme to the matrix setting by producing explicit matrix analogues of the Krawtchouk, Hahn, Meixner, and Charlier polynomials. In particular, we provide the first matrix extensions of the Krawtchouk and Hahn polynomials, filling…

from my link log —
Matrix is email wearing a hoodie.
https://gist.github.com/peterbourgon/04ef9dac27dfc55a0a0fd8e2467874ad
saved 2025-08-01

Matrix ain't it fam
Matrix ain't it fam. GitHub Gist: instantly share code, notes, and snippets.

Soft Unification of Exceptional EFTs in de Sitter space
Zong-Zhe Du
https://arxiv.org/abs/2509.08979 https://arxiv.org/pdf/2509.08979

Soft Unification of Exceptional EFTs in de Sitter space
We uncover a surprising universal soft behaviour (USB) of a de Sitter (dS) S-matrix for \textit{all} exceptional effective field theories (EFTs) in dS space, stemming from the recently proposed generalised energy conservation (GEC) condition. At leading order in the soft limit, the dS S-matrices for all exceptional EFTs exhibit no scaling with the soft momentum, i.e. $\lim_{k\rightarrow 0}\mathcal{A}(k)\sim\mathcal{O}(1)$, a feature we term USB. We specifically show that USB fixes the four-poin…

Photonic Matrix Multiplication Circuit Based on Double Racetrack Resonator Building Blocks
Hussein Talib, Phillip D. Sewell, Ana Vukovic, Sendy Phang
https://arxiv.org/abs/2509.09445

Photonic Matrix Multiplication Circuit Based on Double Racetrack Resonator Building Blocks
This paper presents a novel design framework for photonic matrix multiplication based on programmable photonic integrated circuits using double racetrack (DRT) resonators as building blocks. Here, we analytically demonstrate that the transfer function of the DRT resonator building block resembles that conventional building blocks, such as directional couplers and MZI, making it suitable for building programmable circuits that handle complex matrix calculations. Using this new DRT resonators bui…

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…

Orthogonal Latin Squares of Order Ten with Two Relations: A SAT Investigation
Curtis Bright, Amadou Keita, Brett Stevens
https://arxiv.org/abs/2509.09633 https://

Orthogonal Latin Squares of Order Ten with Two Relations: A SAT Investigation
A $k$-net($n$) is a combinatorial design equivalent to $k-2$ mutually orthogonal Latin squares of order $n$. A relation in a net is a linear dependency over $\mathbb{F}_2$ in the incidence matrix of the net. A computational enumeration of all orthogonal pairs of Latin squares of order 10 whose corresponding nets have at least two nontrivial relations was achieved by Delisle in 2010 and verified by an independent search of Myrvold. In this paper, we confirm the correctness of their exhaustive en…

Crosslisted article(s) found for cs.DS. https://arxiv.org/list/cs.DS/new
[1/1]:
- Instance-Optimal Matrix Multiplicative Weight Update and Its Quantum Applications
Weiyuan Gong, Tongyang Li, Xinzhao Wang, Zhiyu Zhang

A Low-Rank tensor framework for THB-Splines
Tom-Christian Riemer, Martin Stoll
https://arxiv.org/abs/2509.09434 https://arxiv.org/pdf/2509.09434

A Low-Rank tensor framework for THB-Splines
We introduce a low-rank framework for adaptive isogeometric analysis with truncated hierarchical B-splines (THB-splines) that targets the main bottleneck of local refinement: memory- and time-intensive matrix assembly once the global tensor-product structure is lost. The method interpolates geometry-induced weight and source terms in separable spline spaces and computes their tensor-train (TT) representations via the alternating minimal energy (AMEn) solver, enabling level-wise assembly of syst…

Matrix product state classification of 1D multipole symmetry protected topological phases
Takuma Saito, Weiguang Cao, Bo Han, Hiromi Ebisu
https://arxiv.org/abs/2509.09244 https…

Matrix product state classification of 1D multipole symmetry protected topological phases
Spatially modulated symmetries are one of the new types of symmetries whose symmetry actions are position dependent. Yet exotic phases resulting from these spatially modulated symmetries are not fully understood and classified. In this work, we systematically classify one dimensional bosonic symmetry protected topological phases protected respecting multipole symmetries by employing matrix product state formalism. The symmetry action induces projective representations at the ends of an open cha…

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 …

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…

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.

On the Relation of Characteristic Modes of Different Conducting Structures
Leonardo M\"orlein, Dirk Manteuffel
https://arxiv.org/abs/2509.09282 https://

On the Relation of Characteristic Modes of Different Conducting Structures
A formalism is derived to analyze the scattering of a conducting structure based on the characteristic modes of another structure whose surface is a superset of the first structure. This enables the analysis and comparison of different structures using a common basis of characteristic modes. Additionally, it is shown that the scattering matrices and perturbation matrices are no longer diagonal in these cases. Based on this, a modal transformation matrix is defined to describe the mapping betwee…

Determination of CKM matrix element and axial vector form factors from weak decays of quantum-entangled strange baryons
BESIII Collaboration, et al.
https://arxiv.org/abs/2509.09266

Determination of CKM matrix element and axial vector form factors from weak decays of quantum-entangled strange baryons
The electromagnetic structure of the nucleon can be determined from the scattering of electrons off a nucleon target. However, to study its axial structure, neutrino beams are required. The results from these experiments should be extrapolated to zero energy-momentum transfers to access the static properties of the nucleon. For baryons with strange quarks, hyperons, the static limit can instead be approached in semi-leptonic decays, which give direct access to the weak magnetism and axial-vecto…

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…

Just booked the train ticket to #Strasbourg for the #matrix conference 15-18 Oct. Check out the schedule:
https://2025.matrix.org/s…

On the True Significance of the Hubble Tension: A Bayesian Error Decomposition Accounting for Information Loss
Nathalia M. N. da Rocha, Andre L. B. Ribeiro, Francisco B. S. Oliveira
https://arxiv.org/abs/2509.09034

On the True Significance of the Hubble Tension: A Bayesian Error Decomposition Accounting for Information Loss
The Hubble tension, a persistent discrepancy between early and late Universe measurements of $H_0$, poses a significant challenge to the standard cosmological model. In this work, we present a new Bayesian hierarchical framework designed to meticulously decompose this observed tension into its constituent parts: standard measurement errors, information loss arising from parameter-space projection, and genuine physical tension. Our approach, employing Fisher matrix analysis with MCMC-estimated l…

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

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…

Value bounds and Convergence Analysis for Averages of LRP attributions
Alexander Binder, Nastaran Takmil-Homayouni, Urun Dogan
https://arxiv.org/abs/2509.08963 https://

Value bounds and Convergence Analysis for Averages of LRP attributions
We analyze numerical properties of Layer-wise relevance propagation (LRP)-type attribution methods by representing them as a product of modified gradient matrices. This representation creates an analogy to matrix multiplications of Jacobi-matrices which arise from the chain rule of differentiation. In order to shed light on the distribution of attribution values, we derive upper bounds for singular values. Furthermore we derive component-wise bounds for attribution map values. As a main result,…

Determinant of the crossing matrix of a braid
Ayaka Shimizu
https://arxiv.org/abs/2509.08464 https://arxiv.org/pdf/2509.08464

Determinant of the crossing matrix of a braid
In this paper, we define a braid invariant, the purified determinant $P(b)$ of a braid $b$, considering the determinant of the crossing matrix, and show that $P(b_1 b_2)=P(b_2 b_1)$ for any pair of $n$-braids $b_1$ and $b_2$.

Thermodynamic coprocessor for linear operations with input-size-independent calculation time based on open quantum system
I. V. Vovchenko, A. A. Zyablovsky, A. A. Pukhov, E. S. Andrianov
https://arxiv.org/abs/2509.09382

Thermodynamic coprocessor for linear operations with input-size-independent calculation time based on open quantum system
Linear operations, e.g., vector-matrix or vector-vector multiplications, are core operations of modern neural networks. To diminish computational time, these operations are implemented by parallel computations using different coprocessors. In this work we show that open quantum system consisting of bosonic modes and interacting with bosonic reservoirs can be used as analog coprocessor implementing multiple vector-matrix multiplications with stochastic matrices in parallel. Input vectors are enc…

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

A Novel Theoretical Approach on Micro-Nano Robotic Networks Based on Density Matrices and Swarm Quantum Mechanics
Maria Mannone, Mahathi Anand, Peppino Fazio, Abdalla Swikir
https://arxiv.org/abs/2509.08002

A Novel Theoretical Approach on Micro-Nano Robotic Networks Based on Density Matrices and Swarm Quantum Mechanics
In a robotic swarm, parameters such as position and proximity to the target can be described in terms of probability amplitudes. This idea led to recent studies on a quantum approach to the definition of the swarm, including a block-matrix representation. Here, we propose an advancement of the idea, defining a swarm as a mixed quantum state, to be described with a density matrix, whose size does not change with the number of robots. We end the article with some directions for future research.

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 …

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…

Silent Until Sparse: Backdoor Attacks on Semi-Structured Sparsity
Wei Guo, Maura Pintor, Ambra Demontis, Battista Biggio
https://arxiv.org/abs/2509.08747 https://

Silent Until Sparse: Backdoor Attacks on Semi-Structured Sparsity
In the deployment phase, semi-structured sparsity accelerates the execution of deep neural networks on modern GPUs via sparse matrix multiplication. In this paper, targeting the semi-structured sparsity, we introduce a Silent Until Sparse (SUS) backdoor attack, where the released full model remains silent (benign), but becomes a backdoored model after sparsification. The attack operates in two phases: (i) in the backdoor training phase, the backdoor functionality is injected into specific weigh…

An expanded evaluation matrix for the entropy-weight TODIM method to reduce the rank reversal probability and its application in selecting energy storage technology
Lisheng Jiang, Tianyu Zhang, Shiyu Yan, Ran Fang
https://arxiv.org/abs/2509.08236

An expanded evaluation matrix for the entropy-weight TODIM method to reduce the rank reversal probability and its application in selecting energy storage technology
The TODIM method (an acronym in Portuguese for interactive and multiple criteria decision-making) with entropy weights is influenced by rank reversal, a phenomenon where the order of two alternatives changes following the addition of another alternative. Research on rank reversal has predominantly focused on single decision-making methods. To the best of our knowledge, the reduction of rank reversal probability in hybrid methods, such as the entropy-weight TODIM method, remains an unresolved ch…

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. …

Non-trivial Bifocal and Optical Vortex Generation of DNG Materials Unveiled by Generalized Transfer Matrix Method and Matrix Fourier Optics
Yifeng Qin
https://arxiv.org/abs/2509.09150

Non-trivial Bifocal and Optical Vortex Generation of DNG Materials Unveiled by Generalized Transfer Matrix Method and Matrix Fourier Optics
The investigation and analysis of exotic physical phenomena facilitated by metamaterials have emerged as a compelling area of interest in physics, material science, and engineering. However, there remains a lack of suitable theoretical tools for scrutinizing light-matter interactions in metamedia, particularly those involving distinct constitutive tensors, such as polarization cross-coupling or non-reciprocal propagation induced by bianisotropy. In this research, we apply the generalized transf…

\'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…

Variable Matrix-Weighted Besov Spaces
Dachun Yang, Wen Yuan, Zongze Zeng
https://arxiv.org/abs/2509.07786 https://arxiv.org/pdf/2509.07786

Variable Matrix-Weighted Besov Spaces
In this article, applying matrix ${\mathcal A}_{p(\cdot),\infty}$ weights introduced in our previous work, we introduce the matrix-weighted variable Besov space via the matrix weight $W$ or the reducing operators ${\mathbb{A}}$ of order $p(\cdot)$ for $W$, Then we show that, defined either by the matrix weight $W$ or the reducing operators ${\mathbb{A}}$ of order $p(\cdot)$ for $W$, the matrix-weighted variable Besov spaces (respectively, the matrix-weighted variable Besov sequence spaces) are …

Upper block triangular form for the Laplace-Beltrami operator on the special orthogonal group acquired through a flag of trace polynomials spaces
Petre Birtea, Ioan Casu, Dan Comanescu
https://arxiv.org/abs/2509.08391

Upper block triangular form for the Laplace-Beltrami operator on the special orthogonal group acquired through a flag of trace polynomials spaces
The Laplacian of a general trace polynomial function defined on the special orthogonal group $SO(N)$ is explicitly computed. An invariant flag of spaces generated by trace polynomials is constructed. The matrix of the Laplace-Beltrami operator on $SO(N)$ for this flag of vector spaces takes an upper block triangular form. As a consequence of this construction, the eigenvalues and eigenfunctions of the Laplace-Beltrami operator on $SO(N)$ can be computed in an iterative manner. For the particula…

Characterization of the near-null error components utilized in composite adaptive AMG solvers
Austen J. Nelson, Panayot S. Vassilevski
https://arxiv.org/abs/2509.09023 https://

Characterization of the near-null error components utilized in composite adaptive AMG solvers
We provide a theoretical justification for the construction of adaptive composite solvers based on a sequence of AMG (algebraic multigrid) $μ$-cycle methods that exploit error components that the current solver cannot damp efficiently. Each solver component is an aggregation based AMG where its aggregates are constructed using the popular in graph community detection modularity matrix. The latter utilizes the given matrix and the error component vector the current solver cannot handle. The per…

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…

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…

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…

Messenger: Hauptserver von Matrix down
Nach einem Datenbankfehler ist Matrix.org offline. Das Einspielen des Backups ist in Arbeit, eigene Instanzen sind nicht betroffen.
https://www.heise.de/news/Messen…

The Matrix was supposed to be green-ish, but it's been made when the film industry was switching from analog to digital, so all the (re)released versions look different.
Noodle made a charming documentary about losing sanity trying to find the "correct" green:
https://youtu.be/lPU-kXEhSgk

An Algorithmic Upper Bound for Permanents via a Permanental Schur Inequality
Aditi Laddha, Madhusudhan Reddy Pittu
https://arxiv.org/abs/2509.08121 https://

An Algorithmic Upper Bound for Permanents via a Permanental Schur Inequality
Computing the permanent of a non-negative matrix is a computationally challenging, \#P-complete problem with wide-ranging applications. We introduce a novel permanental analogue of Schur's determinant formula, leveraging a newly defined \emph{permanental inverse}. Building on this, we introduce an iterative, deterministic procedure called the \emph{permanent process}, analogous to Gaussian elimination, which yields constructive and algorithmically computable upper bounds on the permanent. Our f…

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

Replaced article(s) found for hep-lat. https://arxiv.org/list/hep-lat/new
[1/1]:
- Lanczos algorithm for lattice QCD matrix elements
Daniel C. Hackett, Michael L. Wagman

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…

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.

Complex moments of the derivative of the Riemann zeta function
Christopher Hughes, Andrew Pearce-Crump
https://arxiv.org/abs/2509.07788 https://arxiv.org/p…

Complex moments of the derivative of the Riemann zeta function
We conjecture results about the complex moments of the derivative of the Riemann zeta function, evaluated at the non-trivial zeros of the Riemann zeta function. We do this via two different random matrix computations. In the first, we find an exact formula for the complex moments of the derivative of the characteristic polynomials of unitary matrices averaged over Haar measure using Selberg's integral. In the second, we consider the hybrid approach for zeta, first proposed by Gonek, Hughes and …

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.

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…

Matrix-variate integer-valued autoregressive processes
Nuo Xu, Kai Yang, Fukang Zhu
https://arxiv.org/abs/2509.07347 https://arxiv.org/pdf/2509.07347

Matrix-variate integer-valued autoregressive processes
In the fields of sociology and economics, the modeling of matrix-variate integervalued time series is urgent. However, no prior studies have addressed the modeling of such data. To address this topic, this paper proposes a novel matrix-variate integer-valued autoregressive model. The key techniques lie in defining two leftand right-matricial thinning operators. The probabilistic and statistical properties of the proposed model are investigated. Furthermore, two estimation methods are developed:…

Woke up to a broken Traefik docker setup (for my Matrix/Element server), it turns out a new version of Docker and Traefik are not playing nice 😔 https://github.com/traefik/traefik/issues/12253

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…

HYLU: Hybrid Parallel Sparse LU Factorization
Xiaoming Chen
https://arxiv.org/abs/2509.07690 https://arxiv.org/pdf/2509.07690

HYLU: Hybrid Parallel Sparse LU Factorization
This article introduces HYLU, a hybrid parallel LU factorization-based general-purpose solver designed for efficiently solving sparse linear systems (Ax=b) on multi-core shared-memory architectures. The key technical feature of HYLU is the integration of hybrid numerical kernels so that it can adapt to various sparsity patterns of coefficient matrices. Tests on 34 sparse matrices from SuiteSparse Matrix Collection reveal that HYLU outperforms Intel MKL PARDISO in the numerical factorization pha…

I spit on the popular opinion and declare #TheMatrixResurrections essential to the original trilogy 🙂 It's not a good Matrix movie. It's a painfully stand-alone movie commenting on society expecting a repeat of a Matrix movie - despite the obvious warnings of The Matrix. Try re-watching it after the first three from today's point view and judge by yourself. F…

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

Datenbank kaputt: Der Hauptserver von Matrix.org ist offline https://www.golem.de/news/datenbank-kaputt-der-hauptserver-von-matrix-org-ist-offline-2509-199737.html

Datenbank kaputt: Der Hauptserver von Matrix.org ist offline - Golem.de
Zahlreiche Anwender können den Chatdienst von Matrix.org derzeit nicht nutzen. Die Organisation hat Probleme mit der Datenbank des Hauptservers.

Frozen differential scattering in reconfigurable complex media
Philipp del Hougne
https://arxiv.org/abs/2509.09506 https://arxiv.org/pdf/2509.09506

Frozen differential scattering in reconfigurable complex media
The sensitivity of transmission to the input wavefront is a hallmark feature of complex media and the basis for wavefront shaping techniques. Yet, intriguing special cases exist in which the output wavefront is "frozen" (agnostic to the input wavefront). This happens when special structure in the complex medium collapses the rank of its transmission matrix to unity. Here, we unveil that an analogous phenomenon exists more universally for differential scattering (including reflection) in reconfi…

General Relativity in IIB Matrix Model
Pei-Ming Ho, Hikaru Kawai, Harold C. Steinacker
https://arxiv.org/abs/2509.06646 https://arxiv.org/pdf/2509.06646

General Relativity in IIB Matrix Model
The matrix models are non-perturbative formulations of string theory, from which many believe that spacetime arises. The matrix fluctuations around the spacetime thus created should represent both matter and gravitational fields. In this paper, we discuss how the gravitational field emerges from the IIB matrix model. In particular, we consider how diffeomorphism invariance arises and how unitarity is guaranteed in this theory. Specifically, we consider matrices as bilocal fields and discuss how…

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…

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…

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…

Error Analysis of Krylov Subspace approximation Based on IDR($s$) Method for Matrix Function Bilinear Forms
Qian Qian Xue, Xiao Qiang Yue, Xian-Ming Gu
https://arxiv.org/abs/2509.08563

Error Analysis of Krylov Subspace approximation Based on IDR($s$) Method for Matrix Function Bilinear Forms
The bilinear form u^\top f(A) v of matrix functions appears in many application problems, where u, v \in R^n\), A \in R^{n * n}\), and f(z) is a given analytic function.The IDR(s) method effectively reduces computational complexity and storage requirements by introducing dimension reduction techniques, while maintaining the numerical stability of the algorithm. This paper studies the numerical algorithm and posterior error estimation for the matrix function bilinear form u^{\top} f(A) v based o…

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…

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…

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…

Quantum variance and fluctuations for Walsh-quantized baker's maps
Laura Shou
https://arxiv.org/abs/2510.08321 https://arxiv.org/pdf/2510.08321

Quantum variance and fluctuations for Walsh-quantized baker's maps
The Walsh-quantized baker's maps are models for quantum chaos on the torus. We show that for all baker's map scaling factors $D\ge2$ except for $D=4$, typically (in the sense of Haar measure on the eigenspaces, which are degenerate) the empirical distribution of the scaled matrix element fluctuations $\sqrt{N}\{\langle φ^{(j)}|\operatorname{Op}_{k,\ell}(a)|φ^{(j)}\rangle-\int_{\mathbb{T}^2}a\}_{j=1}^{N}$ for a random eigenbasis $\{φ^{(j)}\}_{j=1}^{N}$ is asymptotically Gaussian in …

Spatial weights matrix selection and model averaging for multivariate spatial autoregressive models
Xin Miao, Fang Fang, Xuening Zhu, Hansheng Wang
https://arxiv.org/abs/2509.06005

Spatial weights matrix selection and model averaging for multivariate spatial autoregressive models
In this paper, we focus on the model specification problem in multivariate spatial econometric models when a candidate set for the spatial weights matrix is available. We propose a model selection method for the multivariate spatial autoregressive model, when the true spatial weights matrix may not be in the candidates. We show that the selected estimator is asymptotically optimal in the sense of minimizing the squared loss. If the candidate set contains the true spatial weights matrix, the met…

Toward precise $\xi$ gauge fixing for the lattice QCD
Li-Jun Zhou, Dian-Jun Zhao, Wei-jie Fu, Chun-Jiang Shi, Ji-Hao Wang, Yi-Bo Yang
https://arxiv.org/abs/2509.09367 https://…

Toward precise $ξ$ gauge fixing for the lattice QCD
Lattice QCD provides a first-principles framework for solving Quantum Chromodynamics (QCD). However, its application to off-shell partons has been largely restricted to the Landau gauge, as achieving high-precision $ξ$-gauge fixing on the lattice poses significant challenges. Motivated by a universal power-law dependence of off-shell parton matrix elements on gauge-fixing precision in the Landau gauge, we propose an empirical precision extrapolation method to approximate high-precision $ξ$-ga…

Tensor Forms of Derivatives of Matrices and their applications in the Solutions to Differential Equations
Yiran Xu, Guangbin Wang, Changqing Xu
https://arxiv.org/abs/2509.08429 …

Tensor Forms of Derivatives of Matrices and their applications in the Solutions to Differential Equations
We introduce and extend the outer product and contractive product of tensors and matrices, and present some identities in terms of these products. We offer tensor expressions of derivatives of tensors, focus on the tensor forms of derivatives of a matrix w.r.t. another matrix. This tensor form makes possible for us to unify ordinary differential equations (ODEs) with partial differential equations (PDEs), and facilitates solution to them in some cases. For our purpose, we also extend the outer …

Chromium-doped uranium dioxide fuels: A review
Mack Wesley Cleveland, Andrew Nelson, Ericmoore Jossou
https://arxiv.org/abs/2510.07698 https://arxiv.org/pd…

Chromium-doped uranium dioxide fuels: A review
UO2 doped with parts per million Cr2O3 powder is considered a potential near term accident tolerant fuel candidate. Here, the results of decades of industry and academic research into Cr-doped UO2 are analyzed and their shortcomings are critiqued. Focusing on the incorporation mechanisms of Cr into the fuel matrix, we explore a mechanistic understanding of the characteristic properties of Cr-doped UO2, notably, enhanced fission gas retention attributed to enlarged grain sizes following sinterin…

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…

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…

Collocation and Mass Matrix in Least-squares Isogeometric Analysis
Gengchen Li, Hongwei Lin
https://arxiv.org/abs/2509.08192 https://arxiv.org/pdf/2509.081…

Collocation and Mass Matrix in Least-squares Isogeometric Analysis
In this paper, we conduct a systematic numerical analysis of the spectral properties of the collocation and mass matrices in the isogeometric least-squares collocation method (IGA-L), for the approximation of the Poisson problem with homogeneous Dirichlet boundary conditions. This study primarily focuses on the spectral properties of the IGA-L collocation and mass matrices in relation to the isogeometric discretization parameters, such as the mesh size, degree, regularity, spatial dimension, an…

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…

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…

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…

Benchmarking Optimization Algorithms for Automated Calibration of Quantum Devices
Kevin Pack, Shai Machnes, Frank K. Wilhelm
https://arxiv.org/abs/2509.08555 https://

Benchmarking Optimization Algorithms for Automated Calibration of Quantum Devices
We present the results of a comprehensive study of optimization algorithms for the calibration of quantum devices. As part of our ongoing efforts to automate bring-up, tune-up, and system identification procedures, we investigate a broad range of optimizers within a simulated environment designed to closely mimic the challenges of real-world experimental conditions. Our benchmark includes widely used algorithms such as Nelder-Mead and the state-of-the-art Covariance Matrix Adaptation Evolution …

Marginal sets in semigroups and semirings
I. Buchinskiy, M. Kotov, A. Ponmaheshkumar, R. Perumal
https://arxiv.org/abs/2509.06562 https://arxiv.org/pdf/250…

Marginal sets in semigroups and semirings
In 2019, V. A. Roman'kov introduced the concept of marginal sets for groups. He developed a theory of marginal sets and demonstrated how these sets can be applied to improve some key exchange schemes. In this paper, we extend his ideas and introduce the concept of marginal sets for semigroups and semirings. For tropical matrix semigroups and semirings, we describe how some marginal sets can be constructed. We apply marginal sets to improve some key exchange schemes over semigroups.

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Nonnegative matrix factorization and the principle of the common cause
E. Khalafyan, A. E. Allahverdyan, A. Hovhannisyan

Finite projective planes meet spectral gaps
Yuhan Guo, Dong Zhang
https://arxiv.org/abs/2509.06247 https://arxiv.org/pdf/2509.06247

Finite projective planes meet spectral gaps
We show that for any connected graph $G$ with maximum degree $d\ge3$, the spectral gap from $0$ with respect to the adjacency matrix is at most $\sqrt{d-1}$. We further show that the upper bound $\sqrt{d-1}$ is achieved if and only if $G$ is the incidence graph of a finite projective plane of order $d-1$; and for other cases, the upper bound can be improved to $\sqrt{d-2}$. A similar yet more subtle phenomenon involving the normalized Laplacian is also investigated, in which we work on graphs…

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…

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…

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…

Beyond Diagonal IRS Aided OFDM: Rate Maximization under Frequency-Dependent Reflection
Ye Yuan, Shuowen Zhang
https://arxiv.org/abs/2509.06378 https://arxi…

Beyond Diagonal IRS Aided OFDM: Rate Maximization under Frequency-Dependent Reflection
This paper studies a broadband orthogonal frequency division multiplexing (OFDM) system aided by a beyond diagonal intelligent reflecting surface (BD-IRS), where inter-connections exist among different elements such that the reflection matrix can exhibit a beyond diagonal structure. Under practical circuit structures, the reflection matrix of the BD-IRS is generally dependent on the circuit parameters (e.g., capacitance matrix for all tunable capacitors) as well as the operating frequency, whic…

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…

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…

Toughness in regular graphs from eigenvalues
Ruifang Liu, Ao Fan, Jinlong Shu
https://arxiv.org/abs/2510.07007 https://arxiv.org/pdf/2510.07007

Toughness in regular graphs from eigenvalues
The {\it toughness} $τ(G)=\mathrm{min}\{\frac{|S|}{c(G-S)}: S~\mbox{is a vertex cut in}~G\}$ for $G\ncong K_n,$ which was initially proposed by Chvátal in 1973. A graph $G$ is called {\it $t$-tough} if $τ(G)\geq t.$ Let $λ_i(G)$ be the $i$-th largest eigenvalue of the adjacency matrix of a graph $G$. In 1996, Brouwer conjectured that $τ(G)\geq\frac{d}λ-1$ for a connected $d$-regular graph $G,$ where $λ=\mathrm{min}\{|λ_2|, |λ_n|\}.$ Gu [SIAM J. Discrete Ma…

Cosmology, Decoherence and the Second Law
Sebastian Cespedes, Senarath de Alwis, Fernando Quevedo
https://arxiv.org/abs/2509.07077 https://arxiv.org/pdf/25…

Cosmology, Decoherence and the Second Law
We consider quantum decoherence and entropy increase in early universe cosmology. We first study decoherence in a discrete bipartite quantum system for which a single qubit gets entangled with an environment and the entropy increase is correlated with the decay of the off-diagonal terms of the reduced density matrix. We compare this system with continuous systems relevant for cosmology for which there is a natural external intervention, corresponding to the time-dependent separation between the…

Feature Understanding and Sparsity Enhancement via 2-Layered kernel machines (2L-FUSE)
Fabiana Camattari, Sabrina Guastavino, Francesco Marchetti, Emma Perracchione
https://arxiv.org/abs/2509.07806

Feature Understanding and Sparsity Enhancement via 2-Layered kernel machines (2L-FUSE)
We propose a novel sparsity enhancement strategy for regression tasks, based on learning a data-adaptive kernel metric, i.e., a shape matrix, through 2-Layered kernel machines. The resulting shape matrix, which defines a Mahalanobis-type deformation of the input space, is then factorized via an eigen-decomposition, allowing us to identify the most informative directions in the space of features. This data-driven approach provides a flexible, interpretable and accurate feature reduction scheme. …

Depth-Aware Initialization for Stable and Efficient Neural Network Training
Vijay Pandey
https://arxiv.org/abs/2509.05018 https://arxiv.org/pdf/2509.05018

Depth-Aware Initialization for Stable and Efficient Neural Network Training
In past few years, various initialization schemes have been proposed. These schemes are glorot initialization, He initialization, initialization using orthogonal matrix, random walk method for initialization. Some of these methods stress on keeping unit variance of activation and gradient propagation through the network layer. Few of these methods are independent of the depth information while some methods has considered the total network depth for better initialization. In this paper, comprehe…

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…

On the Normalization of Confusion Matrices: Methods and Geometric Interpretations
Johan Erbani, Pierre-Edouard Portier, Elod Egyed-Zsigmond, Sonia Ben Mokhtar, Diana Nurbakova
https://arxiv.org/abs/2509.04959

On the Normalization of Confusion Matrices: Methods and Geometric Interpretations
The confusion matrix is a standard tool for evaluating classifiers by providing insights into class-level errors. In heterogeneous settings, its values are shaped by two main factors: class similarity -- how easily the model confuses two classes -- and distribution bias, arising from skewed distributions in the training and test sets. However, confusion matrix values reflect a mix of both factors, making it difficult to disentangle their individual contributions. To address this, we introduce b…

Large-scale Efficient Molecule Geometry Optimization with Hybrid Quantum-Classical Computing
Yajie Hao, Qiming Ding, Xiaoting Wang, Xiao Yuan
https://arxiv.org/abs/2509.07460 ht…

Large-scale Efficient Molecule Geometry Optimization with Hybrid Quantum-Classical Computing
Accurately and efficiently predicting the equilibrium geometries of large molecules remains a central challenge in quantum computational chemistry, even with hybrid quantum-classical algorithms. Two major obstacles hinder progress: the large number of qubits required and the prohibitive cost of conventional nested optimization. In this work, we introduce a co-optimization framework that combines Density Matrix Embedding Theory (DMET) with Variational Quantum Eigensolver (VQE) to address these l…

Multiparameter quantum metrology at Heisenberg scaling for an arbitrary two-channel linear interferometer with squeezed light
Atmadev Rai, Danilo Triggiani, Paolo Facchi, Vincenzo Tamma
https://arxiv.org/abs/2509.07574

Multiparameter quantum metrology at Heisenberg scaling for an arbitrary two-channel linear interferometer with squeezed light
We present a framework for simultaneously estimating all four real parameters of a general two-channel unitary U(2) with Heisenberg-scaling precision. We derive analytical expressions for the quantum Fisher information matrix and show that all parameters attain the 1/N scaling in the precision by using experimentally feasible Gaussian probes such as two-mode squeezed states or two single-mode squeezed states. Our results extend multiparameter metrology to its most general two-mode setting and e…

Universal quantum control over bosonic network
Zhu-yao Jin, Jun Jing
https://arxiv.org/abs/2509.06560 https://arxiv.org/pdf/2509.06560

Universal quantum control over bosonic network
Perfect transfer of unknown states across distinct nodes is fundamental in the construction of bosonic quantum networks. We here develop a general theory to control an $N$-node bosonic network governed by the time-dependent Hamiltonian, as the universal quantum control theory for continuous-variable systems. In particular, we can activate nonadiabatic passages superposed of initial and target modes by the commutation condition for the Hamiltonian's coefficient matrix in the representation of ti…