Tootfinder

Band-Limited Equivalence of Convolution Operators and its Application to Filtered Vorticity Dynamics
Satori Tsuzuki
https://arxiv.org/abs/2508.14108 https://

Band-Limited Equivalence of Convolution Operators and its Application to Filtered Vorticity Dynamics
In this study, we established a general theorem regarding the equivalence of convolution operators restricted to a finite spectral band. We demonstrated that two kernels with identical Fourier transforms over the resolved band act identically on all band-limited functions, even if their kernels differ outside the band. This property is significant in applied mathematics and computational physics, particularly in scenarios where measurements or simulations are spectrally truncated. As an applica…

Computing-In-Memory Dataflow for Minimal Buffer Traffic
Choongseok Song, Doo Seok Jeong
https://arxiv.org/abs/2508.14375 https://arxiv.org/pdf/2508.14375…

Computing-In-Memory Dataflow for Minimal Buffer Traffic
Computing-In-Memory (CIM) offers a potential solution to the memory wall issue and can achieve high energy efficiency by minimizing data movement, making it a promising architecture for edge AI devices. Lightweight models like MobileNet and EfficientNet, which utilize depthwise convolution for feature extraction, have been developed for these devices. However, CIM macros often face challenges in accelerating depthwise convolution, including underutilization of CIM memory and heavy buffer traffi…

Generalized factorials characterized by Dirichlet convolution
Wanli Ma
https://arxiv.org/abs/2509.13354 https://arxiv.org/pdf/2509.13354

Generalized factorials characterized by Dirichlet convolution
We extend A.B. Mingarelli's method for constructing generalized factorials. Our extension uses a pair of arithmetic functions $(x, y)$, where $x$ is superadditive. When $x$ is the identity function, our generalized factorial reduces to Mingarelli's. A result on the irrationality of the Euler constant within this framework is given. Using Dirichlet convolution, we characterize when two pairs $(α, β)$ and $(x, y)$ generate the same factorials.

Order-generation in posets and convolution of closure operators
Paul Poncet
https://arxiv.org/abs/2509.05299 https://arxiv.org/pdf/2509.05299

Order-generation in posets and convolution of closure operators
Motivated by the Hofmann--Lawson theorem, which states that every continuous lattice is inf-generated by its irreducible elements, we explore how to represent posets by extreme points with respect to a closure operator. For this purpose, we introduce the convolution product of closure operators, and prove that the Krein--Milman property can be transferred from one collection of subsets to another by convolution. This result underpins two new representation theorems of topological flavor, which …

Generalised M\"obius Categories and Convolution Kleene Algebras
James Cranch, Georg Struth, Jana Wagemaker
https://arxiv.org/abs/2509.00168 https://ar…

Generalised Möbius Categories and Convolution Kleene Algebras
Convolution algebras on maps from structures such as monoids, groups or categories into semirings, rings or fields abound in mathematics and the sciences. Of special interest in computing are convolution algebras based on variants of Kleene algebras, which are additively idempotent semirings equipped with a Kleene star. Yet an obstacle to the construction of convolution Kleene algebras on a wide class of structures has so far been the definition of a suitable star. We show that a generalisation…

Categorical Continuous Symmetry
Qiang Jia, Ran Luo, Jiahua Tian, Yi-Nan Wang, Yi Zhang
https://arxiv.org/abs/2509.13170 https://arxiv.org/pdf/2509.13170

Categorical Continuous Symmetry
We define the symmetry category in 1+1d for continuous 0-form $G$-symmetry to be $\textbf{Sky}^τ(G)$, the category of skyscraper sheaves of finite dimensional vector spaces with finite support on the group manifold of $G$, where $τ\in H^4(BG,\mathbb{Z})$ is the anomaly. We propose that the corresponding 2+1d SymTFT is described by the Drinfeld center of $\textbf{Sky}^τ(G)$. We show explicitly the way that $τ$ twists the convolution tensor product of the objects of $\textbf{Sky}^τ(G)$. As a…

A Higher-Order Time Domain Boundary Element Formulation based on Isogeometric Analysis and the Convolution Quadrature Method
Thomas Kramer, Benjamin Marussig, Martin Schanz
https://arxiv.org/abs/2510.06804

A Higher-Order Time Domain Boundary Element Formulation based on Isogeometric Analysis and the Convolution Quadrature Method
An isogeometric boundary element method (BEM) is presented to solve scattering problems in an isotropic homogeneous medium. We consider wave problems governed by the scalar wave equation as in acoustics and the Lamé-Navier equations for elastodynamics considering the theory of linear elasticity. The underlying boundary integral equations imply time-dependent convolution integrals and allow us to determine the sought quantities in the bounded interior or the unbounded exterior after solving for…

On Runge-Kutta convolution quadrature based fractional variational integrators
Khaled Hariz, Sina Ober-Bl\"obaum, Fernando Jimenez
https://arxiv.org/abs/2510.11082 https://…

On Runge-Kutta convolution quadrature based fractional variational integrators
Lagrangian systems subject to fractional damping can be incorporated into a variational formalism. The construction can be made by doubling the state variables and introducing fractional derivatives \cite{JiOb2}. The main objective of this paper is to use the Runge-Kutta convolution quadrature (RKCQ) method for approximating fractional derivatives, combined with higher order Galerkin methods in order to derive fractional variational integrators (FVIs). We are specially interested in the CQ base…

Decoupling Bidirectional Geometric Representations of 4D cost volume with 2D convolution
Xiaobao Wei, Changyong Shu, Zhaokun Yue, Chang Huang, Weiwei Liu, Shuai Yang, Lirong Yang, Peng Gao, Wenbin Zhang, Gaochao Zhu, Chengxiang Wang
https://arxiv.org/abs/2509.02415

Decoupling Bidirectional Geometric Representations of 4D cost volume with 2D convolution
High-performance real-time stereo matching methods invariably rely on 3D regularization of the cost volume, which is unfriendly to mobile devices. And 2D regularization based methods struggle in ill-posed regions. In this paper, we present a deployment-friendly 4D cost aggregation network DBStereo, which is based on pure 2D convolutions. Specifically, we first provide a thorough analysis of the decoupling characteristics of 4D cost volume. And design a lightweight bidirectional geometry aggrega…

Convolution and Graph-based Deep Learning Approaches for Gamma/Hadron Separation in Imaging Atmospheric Cherenkov Telescopes
Abhay Mehta, Dan Parsons, Tim Lukas Holch, David Berge, Matthias Weidlich
https://arxiv.org/abs/2510.05736

Convolution and Graph-based Deep Learning Approaches for Gamma/Hadron Separation in Imaging Atmospheric Cherenkov Telescopes
The identification of $γ$-rays from the predominant hadronic-background is a key aspect in their ground-based detection using Imaging Atmospheric Cherenkov Telescopes (IACTs). While current methods are limited in their ability to exploit correlations in complex data, deep learning-based models offer a promising alternative by directly leveraging image-level information. However, several challenges involving the robustness and applicability of such models remain. Designing model architectures w…

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 …

On the connection between Bochner's theorem on positive definite maps and Choi theorem on complete positivity
Sohail, Sahil
https://arxiv.org/abs/2509.02529 https://…

On the connection between Bochner's theorem on positive definite maps and Choi theorem on complete positivity
In this work, we establish a connection between Bochner's theorem on positive definite maps and Choi theorem on complete positivity. We begin by defining a convolution product between maps from the contracted semigroup algebra $\mathbb{C}_0[S]$ of a semigroup $S$ to an arbitrary associative algebra $\mathcal{A}$. The convolution product makes the space $L(\mathbb{C}_0[S],\mathcal{A})$ of linear maps from $\mathbb{C}_0[S]$ to $\mathcal{A}$ an associative algebra. We prove that the convolution al…

Closed-Form Least-Squares Design of Fast-Convolution Based Variable-Bandwidth FIR Filters
Oksana Moryakova, H{\aa}kan Johansson
https://arxiv.org/abs/2509.25931 https://

Closed-Form Least-Squares Design of Fast-Convolution Based Variable-Bandwidth FIR Filters
This paper introduces a closed-form least-squares (LS) design approach for fast-convolution (FC) based variable-bandwidth (VBW) finite-impulse-response (FIR) filters. The proposed LS design utilizes frequency sampling and the VBW filter frequency-domain implementation using the overlap-save (OLS) method, that together offer significant savings in implementation and online bandwidth reconfiguration complexities. Since combining frequency-domain design and OLS implementation leads to a linear per…

Leveraging Language Semantics for Collaborative Filtering with TextGCN and TextGCN-MLP: Zero-Shot vs In-Domain Performance
Andrei Chernov, Haroon Wahab, Oleg Novitskij
https://arxiv.org/abs/2510.12461 …

Leveraging Language Semantics for Collaborative Filtering with TextGCN and TextGCN-MLP: Zero-Shot vs In-Domain Performance
In recent years, various approaches have been proposed to leverage large language models (LLMs) for incorporating textual information about items into recommender systems. Existing methods primarily focus on either fine-tuning LLMs to generate recommendations or integrating LLM-based embeddings into downstream models. In this work, we follow the latter direction and propose \textbf{TextGCN}, which applies parameter-free graph convolution layers directly over LLM-based item-title embeddings, ins…

Modeling the TMD shape function in $J/\psi$ electroproduction
Miguel G. Echevarria, Raj Kishore, Samuel F. Romera
https://arxiv.org/abs/2510.11809 https://…

Modeling the TMD shape function in $J/ψ$ electroproduction
The next-to-leading order hard function for quarkonium electroproduction is calculated within the framework of transverse-momentum-dependent (TMD) factorization in the low-transverse-momentum regime. The structure of the TMD shape function in quarkonium leptoproduction is analyzed through its operator-level definition. Particular attention is given to the convolution of the unpolarized TMD gluon distribution with the TMD shape function, thereby illustrating the latter's phenomenological role. B…

Triple convolution sums of the generalised divisor functions
Bikram Misra, Biswajyoti Saha
https://arxiv.org/abs/2509.04610 https://arxiv.org/pdf/2509.0461…

Triple convolution sums of the generalised divisor functions
We study the triple convolution sum of the generalised divisor functions given by $$\sum_{n\leq x} d_k(n+h)d_l(n)d_m(n-h),$$ where $h \le x^{1-ε}$ for any $ε>0$ and $d_k(n)$ denotes the generalised divisor function which counts the number of ways $n$ can be written as product of $k$ many positive integers. The purpose of this paper is two-fold. Firstly, we note a predicted asymptotic estimate for the above sum, where the constant appearing in the estimate can be obtained from the theory of Di…

Benchmarking Deep Learning Convolutions on Energy-constrained CPUs
Enrique Galvez (ALSOC), Adrien Cassagne (ALSOC), Alix Munier (ALSOC), Manuel Bouyer
https://arxiv.org/abs/2509.26217

Benchmarking Deep Learning Convolutions on Energy-constrained CPUs
This work evaluates state-of-the-art convolution algorithms for CPU-based deep learning inference. While most prior studies focus on GPUs or NPUs, CPU implementations remain relatively underoptimized. We benchmark direct, GEMM-based, and Winograd convolutions across modern CPUs from ARM __ , Intel __ , AMD __ , Apple __ , and Nvidia __ , considering both latency and energy efficiency. Our results highlight the key architectural factors that govern CPU efficiency for convolution operations, prov…

Mean-field Modeling of Social Interactions Using Classical Density Functional Theory
Ziheng Xu, Shenggao Zhou
https://arxiv.org/abs/2509.05979 https://arxi…

Mean-field Modeling of Social Interactions Using Classical Density Functional Theory
Incorporating social interactions is essential to an accurate modeling of epidemic spreading. This work proposes a novel local mean-field density functional theory model by using the sum-of-exponential approximation of convolution kernels for social interactions, which in turn converts the convolution terms into interaction potentials that are governed by the Debye-Hückel equation. Thanks to the local formulation of the proposed model, linear stability analysis is able to derive a novel instab…

Fine dissipative properties of Euler solutions with measure first derivatives
Marco Inversi
https://arxiv.org/abs/2510.10704 https://arxiv.org/pdf/2510.107…

Fine dissipative properties of Euler solutions with measure first derivatives
We study fine properties of bounded weak solutions to the incompressible Euler equations whose first derivatives, or only some combinations of them, are Radon measures. As consequences we obtain elementary proofs of the local energy conservation for solutions in BV and BD, without relying on the freedom in choosing the convolution kernel appearing in the approximation of the dissipation. The argument heavily exploits the form of the Euler nonlinearity and it does not apply to the linear transpo…

Dual-Branch Convolutional Framework for Spatial and Frequency-Based Image Forgery Detection
Naman Tyagi
https://arxiv.org/abs/2509.05281 https://arxiv.org/…

Dual-Branch Convolutional Framework for Spatial and Frequency-Based Image Forgery Detection
With a very rapid increase in deepfakes and digital image forgeries, ensuring the authenticity of images is becoming increasingly challenging. This report introduces a forgery detection framework that combines spatial and frequency-based features for detecting forgeries. We propose a dual branch convolution neural network that operates on features extracted from spatial and frequency domains. Features from both branches are fused and compared within a Siamese network, yielding 64 dimensional em…

Replaced article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- A Lightweight Convolution and Vision Transformer integrated model with Multi-scale Self-attention...
Yi Zhang, Lingxiao Wei, Bowei Zhang, Ziwei Liu, Kai Yi, Shu Hu

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Flexible Coded Distributed Convolution Computing for Enhanced Straggler Resilience and Numerical ...
Shuo Tan, Rui Liu, Xuesong Han, XianLei Long, Kai Wan, Linqi Song, Yong Li

An Efficient Deep Template Matching and In-Plane Pose Estimation Method via Template-Aware Dynamic Convolution
Ke Jia, Ji Zhou, Hanxin Li, Zhigan Zhou, Haojie Chu, Xiaojie Li
https://arxiv.org/abs/2510.01678

An Efficient Deep Template Matching and In-Plane Pose Estimation Method via Template-Aware Dynamic Convolution
In industrial inspection and component alignment tasks, template matching requires efficient estimation of a target's position and geometric state (rotation and scaling) under complex backgrounds to support precise downstream operations. Traditional methods rely on exhaustive enumeration of angles and scales, leading to low efficiency under compound transformations. Meanwhile, most deep learning-based approaches only estimate similarity scores without explicitly modeling geometric pose, making …

Banach algebra structure in Hardy-Carleson type tent spaces and Ces\`aro-like operators
Rong Yang, Xiangling Zhu
https://arxiv.org/abs/2509.09379 https://a…

Banach algebra structure in Hardy-Carleson type tent spaces and Cesàro-like operators
In this paper, the Hadamard-Bergman convolution and Banach algebra structure by the Duhamel product on Hardy-Carleson type tent spaces was investigated. Moreover, the boundedness and compactness of the Cesàro-like operator $\mathcal{C}_μ$ on Hardy-Carleson type tent spaces $AT_p^\infty(α)$ are also studied.

LORT: Locally Refined Convolution and Taylor Transformer for Monaural Speech Enhancement
Junyu Wang, Zizhen Lin, Tianrui Wang, Meng Ge, Longbiao Wang, Jianwu Dang
https://arxiv.org/abs/2509.23832

LORT: Locally Refined Convolution and Taylor Transformer for Monaural Speech Enhancement
Achieving superior enhancement performance while maintaining a low parameter count and computational complexity remains a challenge in the field of speech enhancement. In this paper, we introduce LORT, a novel architecture that integrates spatial-channel enhanced Taylor Transformer and locally refined convolution for efficient and robust speech enhancement. We propose a Taylor multi-head self-attention (T-MSA) module enhanced with spatial-channel enhancement attention (SCEA), designed to facili…

A refined variant of Hartley convolution: Algebraic structures and related issues
Trinh Tuan
https://arxiv.org/abs/2509.17529 https://arxiv.org/pdf/2509.17…

A refined variant of Hartley convolution: Algebraic structures and related issues
In this work, we propose a novel convolution product associated with the $\mathscr{H}$-transform, denoted by $\underset{\mathscr{H}}{\ast}$, and explore its fundamental properties. Here, the $\mathscr{H}$-transform may be regarded as a refined variant of the classical Fourier, Hartley transform, with kernel function depending on two parameters $a,b$. Our first contribution shows that the space of integrable functions, equipped with multiplication given by the $\underset{\mathscr{H}}{\ast}$-conv…

Replaced article(s) found for cs.MS. https://arxiv.org/list/cs.MS/new
[1/1]:
- Permutation-Avoiding FFT-Based Convolution
Nicolas Venkovic, Hartwig Anzt
https://

Crosslisted article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Generalised M\"obius Categories and Convolution Kleene Algebras
James Cranch, Georg Struth, Jana Wagemaker

Approximation by neural network operators of convolution type activated by deformed and parametrized half hyperbolic tangent function
Asiye Arif, Tugba Yurdakadim
https://arxiv.org/abs/2510.05980

Approximation by neural network operators of convolution type activated by deformed and parametrized half hyperbolic tangent function
Here, we introduce three kinds of neural network operators of convolution type which are activated by q-deformed and \b{eta}-parametrized half hyperbolic tangent function. We obtain quantitative convergence results to the identity operator with the use of modulus of continuity. Global smoothness preservation of our operators are also presented and the iterated versions of them are taken into the consideration.

On necessary conditions of rational-infinite divisibility for distributions with non-zero discrete parts
Alexey Khartov
https://arxiv.org/abs/2509.07244 https://

On necessary conditions of rational-infinite divisibility for distributions with non-zero discrete parts
We consider the new class $\boldsymbol{Q}$ of rational-infinitely (or quasi-infinitely) divisible distribution functions on the real line. By definition, $F\in \boldsymbol{Q}$ if there are some infinitely divisible distribution functions $F_1$ and $F_2$ such that $F_1=F*F_2$, where ``$*$'' is the convolution. The characteristic function of such $F$ admits the Lévy--Khintchine-type representation with a ``signed spectral measure''. The class $\boldsymbol{Q}$ is a significant extension of the fa…

UltraScatter: Ray-Based Simulation of Ultrasound Scattering
Felix Duelmer, Mohammad Farid Azampour, Nassir Navab
https://arxiv.org/abs/2510.10612 https://a…

UltraScatter: Ray-Based Simulation of Ultrasound Scattering
Traditional ultrasound simulation methods solve wave equations numerically, achieving high accuracy but at substantial computational cost. Faster alternatives based on convolution with precomputed impulse responses remain relatively slow, often requiring several minutes to generate a full B-mode image. We introduce UltraScatter, a probabilistic ray tracing framework that models ultrasound scattering efficiently and realistically. Tissue is represented as a volumetric field of scattering probabi…

Crosslisted article(s) found for math.GN. https://arxiv.org/list/math.GN/new
[1/1]:
- Order-generation in posets and convolution of closure operators
Paul Poncet

Identifying Microlensing by Compact Dark Matter through Diffraction Patterns in Gravitational Waves with Machine Learning
Ao Liu, Tonghua Liu, Dejiang Li, Cuihong Wen, Jieci Wang, Kai Liao, Jiaxing Cui, Huan Zhou
https://arxiv.org/abs/2509.04538

Identifying Microlensing by Compact Dark Matter through Diffraction Patterns in Gravitational Waves with Machine Learning
Gravitational wave lensing, particularly microlensing by compact dark matter (DM), offers a unique avenue to probe the nature of dark matter. However, conventional detection methods are often computationally expensive, inefficient, and sensitive to waveform systematics. In this work, we introduce the Wavelet Convolution Detector (WCD), a deep learning framework specifically designed to identify wave-optics diffraction patterns imprinted in gravitationally lensed signals. The WCD integrates mult…

GMTHRASHpy: Forward Convolutions of Crossed Molecular Beams Experiments in Python
Kazuumi Fujioka, Rui Sun
https://arxiv.org/abs/2510.05398 https://arxiv.o…

GMTHRASHpy: Forward Convolutions of Crossed Molecular Beams Experiments in Python
We introduce GMTHRASHpy, a Python-based application to do forward convolution fits of crossed molecular beams experiments. The code is designed to be easy-to-use and widely-available, so as to be of value to anyone wanting to reproduce data or fits from these experiments. GMTHRASHpy has been benchmarked to replicate the original GMTHRASH executable for a wide variety of published reactions. The inputs and algorithm guiding it are explained. The code is open-source and can be downloaded at: gith…

A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients
Jingyang Shan, Qishuai Yu, Jiacen Liu, Shaolin Zhang, Wen Shen, Yanxiao Zhao, Tianyi Wang, Xiaolin Qin, Yiheng Yin
https://arxiv.org/abs/2509.02256

A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients
Neck pain is the primary symptom of cervical spondylosis, yet its underlying mechanisms remain unclear, leading to uncertain treatment outcomes. To address the challenges of multimodal feature fusion caused by imaging differences and spatial mismatches, this paper proposes an Adaptive Bidirectional Pyramid Difference Convolution (ABPDC) module that facilitates multimodal integration by exploiting the advantages of difference convolution in texture extraction and grayscale invariance, and a Feat…

A Multi-Scale Feature Extraction and Fusion UNet for Pathloss Prediction in UAV-Assisted mmWave Radio Networks
Sajjad Hussain
https://arxiv.org/abs/2509.09606 https://

A Multi-Scale Feature Extraction and Fusion UNet for Pathloss Prediction in UAV-Assisted mmWave Radio Networks
Accurate pathloss prediction is essential for the design and optimization of UAV-assisted millimeter-wave (mmWave) networks. While deep learning approaches have shown strong potential, their generalization across diverse environments, robustness to noisy inputs, and sensitivity to UAV altitude remain underexplored. To address these challenges, we propose a UNet-based deep learning architecture that combines multi-scale feature extraction, convolution-based feature fusion, and an atrous spatial …

Quantum-Theoretical Re-interpretation of Pricing Theory
Tian Xin
https://arxiv.org/abs/2510.06287 https://arxiv.org/pdf/2510.06287

Quantum-Theoretical Re-interpretation of Pricing Theory
Motivated by Heisenberg's observable-only stance, we replace latent "information" (filtrations, hidden diffusions, state variables) with observable transitions between price states. On a discrete price lattice with a Hilbert-space representation, shift operators and the spectral calculus of the price define observable frequency operators and a translation-invariant convolution generator. Combined with jump operators that encode transition intensities, this yields a completely positive, translat…

A Duflo-Moore theorem for ergodic group actions on semifinite von Neumann algebras
Ulrik Enstad, Hannes Wendt
https://arxiv.org/abs/2508.15575 https://arxi…

A Duflo-Moore theorem for ergodic group actions on semifinite von Neumann algebras
We prove a generalization of the orthogonality relations of Duflo and Moore for ergodic, trace-preserving group actions on von Neumann algebras that are integrable in a suitable sense. We also obtain convolution inequalities that generalize both Young's inequality for convolution on locally compact groups and inequalities for operator-operator convolutions in Werner's quantum harmonic analysis.

High Utilization Energy-Aware Real-Time Inference Deep Convolutional Neural Network Accelerator
Kuan-Ting Lin, Ching-Te Chiu, Jheng-Yi Chang, Shi-Zong Huang, Yu-Ting Li
https://arxiv.org/abs/2509.05688

High Utilization Energy-Aware Real-Time Inference Deep Convolutional Neural Network Accelerator
Deep convolution Neural Network (DCNN) has been widely used in computer vision tasks. However, for edge devices even inference has too large computational complexity and data access amount. The inference latency of state-of-the-art models are impractical for real-world applications. In this paper, we propose a high utilization energy-aware real-time inference deep convolutional neural network accelerator, which improves the performance of the current accelerators. First, we use the 1x1 size con…

CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy
Cunmin Zhao, Ziyuan Luo, Guoye Guan, Zelin Li, Yiming Ma, Zhongying Zhao, Renjie Wan
https://arxiv.org/abs/2508.19300

CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy
4D live fluorescence microscopy is often compromised by prolonged high intensity illumination which induces photobleaching and phototoxic effects that generate photo-induced artifacts and severely impair image continuity and detail recovery. To address this challenge, we propose the CellINR framework, a case-specific optimization approach based on implicit neural representation. The method employs blind convolution and structure amplification strategies to map 3D spatial coordinates into the hi…

Spatial two-grid compact difference scheme for two-dimensional nonlinear diffusion-wave equations with variable exponent
Hao Zhang, Kexin Li, Wenlin Qiu
https://arxiv.org/abs/2510.12188

Spatial two-grid compact difference scheme for two-dimensional nonlinear diffusion-wave equations with variable exponent
This paper presents a spatial two-grid (STG) compact difference scheme for a two-dimensional (2D) nonlinear diffusion-wave equation with variable exponent, which describes, e.g., the propagation of mechanical diffusive waves in viscoelastic media with varying material properties. Following the idea of the convolution approach, the diffusion-wave model is first transformed into an equivalent formulation. A fully discrete scheme is then developed by applying a compact difference approximation in …

Smoothed Shifted Convolutions of Generalised Divisor Functions
Cheuk Fung Lau
https://arxiv.org/abs/2509.07556 https://arxiv.org/pdf/2509.07556

Smoothed Shifted Convolutions of Generalised Divisor Functions
We prove an asymptotic formula for the smoothed shifted convolution of the generalised divisor function $d_k(n)$ and the divisor function $d(n)$, with a power-saving error term independent of $k$. In particular, when $k$ is large, this is an improvement on Topacogullari (2018).

HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation
Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich, Cyril Joly, Jean-Emmanuel Deschaud
https://arxiv.org/abs/2510.06876

HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation
LiDAR semantic segmentation is crucial for autonomous vehicles and mobile robots, requiring high accuracy and real-time processing, especially on resource-constrained embedded systems. Previous state-of-the-art methods often face a trade-off between accuracy and speed. Point-based and sparse convolution-based methods are accurate but slow due to the complexity of neighbor searching and 3D convolutions. Projection-based methods are faster but lose critical geometric information during the 2D pro…

One algebra of double cosets for a general linear group over a finite field
Yury A. Neretin
https://arxiv.org/abs/2508.16502 https://arxiv.org/pdf/2508.165…

One algebra of double cosets for a general linear group over a finite field
Let $\mathbb {F}_q$ be finite field with $q$ elements. Let $α\leqslant n$ be positive integers. Consider the general linear group $\mathrm{GL}(α+n, \mathbb {F}_q) $ and its subgroup $H(n)$, which fixes the first $α$ basis elements in $\mathbb {F}_q^{α+n}$. Denote $\mathcal{A}_n$ by the convolution algebra of $H(n)$-biinvariant functions on $\mathrm{GL}(α+n, \mathbb {F}_q) $. We describe algebras $\mathcal{A}_n$ in terms of generators and relations and show that the family $\mathcal{A}_n$ a…

Boolean and Free Symmetrization of Bernoulli Laws
Sukrit Chakraborty
https://arxiv.org/abs/2508.17112 https://arxiv.org/pdf/2508.17112

Boolean and Free Symmetrization of Bernoulli Laws
We revisit the notion of \emph{symmetrizers} for Bernoulli random variables, originally studied in the classical convolution setting by Harremoës and Vignat \cite{HarremoesVignat2008} and later refined in Dossani \cite{Dossani2008}. In the classical case, an asymmetric Bernoulli distribution is \emph{symmetry resistant}: any independent symmetrizer must have variance at least $pq$, where $p$ is the Bernoulli parameter and $q=1-p$. We prove an analogous result for \emph{Boolean convolution}, sh…

Crosslisted article(s) found for hep-ex. https://arxiv.org/list/hep-ex/new
[1/1]:
- Binary Classification of Light and Dark Time Traces of a Transition Edge Sensor Using Convolution...
Rivasto, Isleif, Januschek, Lindner, Meyer, Othman, Gimeno, Schwemmbauer

Global Existence of Solutions of Nonlocal Geirer-Meinhardt Model and Effect of Nonlocal Operator in Pattern Formation
Md Shah Alam
https://arxiv.org/abs/2509.15598 https://

Global Existence of Solutions of Nonlocal Geirer-Meinhardt Model and Effect of Nonlocal Operator in Pattern Formation
We study the global existence of solutions to a class of nonlocal Geirer-Meinhardt system. This is a two component reaction-diffusion model on a bounded domain in $\mathbb{R}^n$, $n \ge 1$, with nonlocal diffusion given by a nonlocal convolution operator. We have used semigroup theory and derive estimate to guarantee global existence. Then we build an $L^b$ functional to bound our solution independent of the nonlocal convolution kernel for $2 \le b < \infty$. Next, we have used this result to o…

Anticyclotomic diagonal classes and Beilinson--Flach elements
Ra\'ul Alonso, Lois Omil-Pazos, \'Oscar Rivero
https://arxiv.org/abs/2509.07564 https://

Anticyclotomic diagonal classes and Beilinson--Flach elements
We present a comparison between the anticyclotomic Euler system of diagonal cycles associated with the convolution of two modular forms and the cyclotomic Beilinson--Flach Euler system. This extends the seminal work of Bertolini, Darmon, and Venerucci, who established a link between (anticyclotomic) Heegner points and the Beilinson--Kato system. Our approach hinges on a detailed analysis of $p$-adic $L$-functions and Perrin-Riou maps and exploits the Eisenstein degeneration of diagonal cycles a…

FDTRImageEnhancer: Combining Physics-Informed Deconvolution and Microstructure-Aware Deep Learning to Enhance Thermal Images
Alesanmi Richmond Rerelope Odufisan
https://arxiv.org/abs/2508.16590

FDTRImageEnhancer: Combining Physics-Informed Deconvolution and Microstructure-Aware Deep Learning to Enhance Thermal Images
We present FDTRImageEnhancer, an open-source computational framework that improves thermal conductivity mapping from Frequency Domain ThermoReflectance (FDTR) phase data by integrating a physics-based Gaussian convolution abstraction with microstructure-aware deep learning. The Gaussian kernel models the spatial averaging effects of pump and probe beams, while k-means clustering of high-resolution structural images reduces the parameter space for inverse modeling. A physics-informed neural netw…

The Hadamard parametrix on half-Minkowski with Robin boundary conditions: Fundamental solutions and Hadamard states
B. Costeri, C. Dappiaggi, B. A. Ju\'arez-Aubry, R. D. Singh
https://arxiv.org/abs/2509.26035

The Hadamard parametrix on half-Minkowski with Robin boundary conditions: Fundamental solutions and Hadamard states
We address the problem of constructing fundamental solutions and Hadamard states for a Klein-Gordon field in half-Minkowski spacetime with Robin boundary conditions in $d \geq 2$ spacetime dimensions. First, using a generalisation of the Robin-to-Dirichlet map exploited by Bondurant and Fulling [J. Phys. A: Math. Theor. {\bf 38} 7 (2005)] in dimension $2$, we obtain a representation for the advanced and retarded Green operators in terms of a convolution with the kernel of the inverse Robin-to-D…

Conv-like Scale-Fusion Time Series Transformer: A Multi-Scale Representation for Variable-Length Long Time Series
Kai Zhang, Siming Sun, Zhengyu Fan, Qinmin Yang, Xuejun Jiang
https://arxiv.org/abs/2509.17845

Conv-like Scale-Fusion Time Series Transformer: A Multi-Scale Representation for Variable-Length Long Time Series
Time series analysis faces significant challenges in handling variable-length data and achieving robust generalization. While Transformer-based models have advanced time series tasks, they often struggle with feature redundancy and limited generalization capabilities. Drawing inspiration from classical CNN architectures' pyramidal structure, we propose a Multi-Scale Representation Learning Framework based on a Conv-like ScaleFusion Transformer. Our approach introduces a temporal convolution-lik…

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach
Obasho M, Shambhavi Jaiswal, Santanu Das, Krishna Mohan Parattu
https://arxiv.org/abs/2509.00139

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach
Extracting cosmological information from microwave sky observations requires accurate estimation of the underlying Cosmic Microwave Background (CMB) by removing foreground contamination, instrumental noise, and the effects of beam convolution. In this work, we develop a machine learning-based approach for CMB reconstruction using a generative adversarial network (GAN) architecture, where the generator is modeled as a U-Net-based convolutional neural network. To train the network, we generate …

SpecGuard: Spectral Projection-based Advanced Invisible Watermarking
Inzamamul Alam, Md Tanvir Islam, Khan Muhammad, Simon S. Woo
https://arxiv.org/abs/2510.07302 https://

SpecGuard: Spectral Projection-based Advanced Invisible Watermarking
Watermarking embeds imperceptible patterns into images for authenticity verification. However, existing methods often lack robustness against various transformations primarily including distortions, image regeneration, and adversarial perturbation, creating real-world challenges. In this work, we introduce SpecGuard, a novel watermarking approach for robust and invisible image watermarking. Unlike prior approaches, we embed the message inside hidden convolution layers by converting from the spa…

Nonlocal effective field theory and its applications
P. Wang, Zhengyang Gao, Fangcheng He, Chueng-Ryong Ji, W. Melnitchouk, Y. Salamu
https://arxiv.org/abs/2509.25657 https://…

Nonlocal effective field theory and its applications
We review recent applications of nonlocal effective field theory, focusing in particular on nonlocal chiral effective theory and nonlocal quantum electrodynamics (QED), as well as an extension of nonlocal effective theory to curved spacetime. For the chiral effective theory, we discuss the calculation of generalized parton distributions (GPDs) of the nucleon at nonzero skewness, along with the corresponding gravitational (or mechanical) form factors, within the convolution framework. In the QED…

A Resource-Driven Approach for Implementing CNNs on FPGAs Using Adaptive IPs
Philippe Magalh\~aes (LabHC), Virginie Fresse (LabHC), Beno\^it Suffran (LabHC), Olivier Alata (LabHC)
https://arxiv.org/abs/2510.02990

A Resource-Driven Approach for Implementing CNNs on FPGAs Using Adaptive IPs
The increasing demand for real-time, low-latency artificial intelligence applications has propelled the use of Field-Programmable Gate Arrays (FPGAs) for Convolutional Neural Network (CNN) implementations. FPGAs offer reconfigurability, energy efficiency, and performance advantages over GPUs, making them suitable for edge devices and embedded systems. This work presents a novel library of resource-efficient convolution IPs designed to automatically adapt to the available FPGA resources. Develop…

Generalized Meixner-type free gamma distributions:convolution formulas and potential correspondence
Noriyoshi Sakuma, Yuki Ueda
https://arxiv.org/abs/2508.15585 https://

Generalized Meixner-type free gamma distributions:convolution formulas and potential correspondence
We introduce and study a class of generalized Meixner-type free gamma distributions $μ_{t,θ,λ}$ ($t,θ>0$ and $λ\ge 1$), which includes both the free gamma distributions introduced by Anshelevich and certain scaled free beta prime distributions introduced by Yoshida. We investigate fundamental properties and mixture structures of these distributions. In particular, we consider the Gibbs distribution $\frac{1}{\mathcal{Z}_{t,θ,λ}} \exp\{-V_{t,θ,λ}(x)\}$ associated with a family of potent…

DeRainMamba: A Frequency-Aware State Space Model with Detail Enhancement for Image Deraining
Zhiliang Zhu, Tao Zeng, Tao Yang, Guoliang Luo, Jiyong Zeng
https://arxiv.org/abs/2510.06746

DeRainMamba: A Frequency-Aware State Space Model with Detail Enhancement for Image Deraining
Image deraining is crucial for improving visual quality and supporting reliable downstream vision tasks. Although Mamba-based models provide efficient sequence modeling, their limited ability to capture fine-grained details and lack of frequency-domain awareness restrict further improvements. To address these issues, we propose DeRainMamba, which integrates a Frequency-Aware State-Space Module (FASSM) and Multi-Directional Perception Convolution (MDPConv). FASSM leverages Fourier transform to d…

On Hardy spaces associated with the twisted Laplacian and sharp estimates for the corresponding wave operator
Riju Basak, K. Jotsaroop
https://arxiv.org/abs/2509.00327 https://

On Hardy spaces associated with the twisted Laplacian and sharp estimates for the corresponding wave operator
We prove various equivalent characterisations of the Hardy space $H^p_{\mathcal{L}}(\mathbb{C}^n)$ for $00$ on $H^p_{\mathcal{L}}(\mathbb{C}^n)$. M…

Uncertainty-Gated Deformable Network for Breast Tumor Segmentation in MR Images
Yue Zhang, Jiahua Dong, Chengtao Peng, Qiuli Wang, Dan Song, Guiduo Duan
https://arxiv.org/abs/2509.15758

Uncertainty-Gated Deformable Network for Breast Tumor Segmentation in MR Images
Accurate segmentation of breast tumors in magnetic resonance images (MRI) is essential for breast cancer diagnosis, yet existing methods face challenges in capturing irregular tumor shapes and effectively integrating local and global features. To address these limitations, we propose an uncertainty-gated deformable network to leverage the complementary information from CNN and Transformers. Specifically, we incorporates deformable feature modeling into both convolution and attention modules, en…

Accelerated Interactive Auralization of Highly Reverberant Spaces using Graphics Hardware
Hannes Rosseel, Toon van Waterschoot
https://arxiv.org/abs/2509.04390 https://

Accelerated Interactive Auralization of Highly Reverberant Spaces using Graphics Hardware
Interactive acoustic auralization allows users to explore virtual acoustic environments in real-time, enabling the acoustic recreation of concert hall or Historical Worship Spaces (HWS) that are either no longer accessible, acoustically altered, or impractical to visit. Interactive acoustic synthesis requires real-time convolution of input signals with a set of synthesis filters that model the space-time acoustic response of the space. The acoustics in concert halls and HWS are both characteriz…

A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction
Shalini Dangi, Surya Karthikeya Mullapudi, Chandravardhan Singh Raghaw, Shahid Shafi Dar, Mohammad Zia Ur Rehman, Nagendra Kumar
https://arxiv.org/abs/2509.15966

A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction
Precise yield prediction is essential for agricultural sustainability and food security. However, climate change complicates accurate yield prediction by affecting major factors such as weather conditions, soil fertility, and farm management systems. Advances in technology have played an essential role in overcoming these challenges by leveraging satellite monitoring and data analysis for precise yield estimation. Current methods rely on spatio-temporal data for predicting crop yield, but they …

Application of Quantum Convolutional Neural Networks for MRI-Based Brain Tumor Detection and Classification
Sugih Pratama Nugraha, Ariiq Islam Alfajri, Tony Sumaryada, Duong Thanh Tai, Nissren Tamam, Abdelmoneim Sulieman, Sitti Yani
https://arxiv.org/abs/2509.02582

Application of Quantum Convolutional Neural Networks for MRI-Based Brain Tumor Detection and Classification
This study explores the application of Quantum Convolutional Neural Networks (QCNNs) for brain tumor classification using MRI images, leveraging quantum computing for enhanced computational efficiency. A dataset of 3,264 MRI images, including glioma, meningioma, pituitary tumors, and non-tumor cases, was utilized. The data was split into 80% training and 20% testing, with an oversampling technique applied to address class imbalance. The QCNN model consists of quantum convolution layers, flatten…

Crosslisted article(s) found for math.OA. https://arxiv.org/list/math.OA/new
[1/1]:
- Generalized Meixner-type free gamma distributions:convolution formulas and potential correspondence
Noriyoshi Sakuma, Yuki Ueda

A nonlocal Aw-Rascle-Zhang system with linear pressure term
Debora Amadori, Felisia Angela Chiarello, Gianmarco Cipollone
https://arxiv.org/abs/2509.20973 https://

A nonlocal Aw-Rascle-Zhang system with linear pressure term
In this paper, we study a nonlocal extension of the Aw-Rascle-Zhang traffic model, where the pressure-like term is modeled as a convolution between vehicle density and a kernel function. This formulation captures nonlocal driver interactions and aligns structurally with the Euler-alignment system studied in [23]. Using a sticky particle approximation, we construct entropy solutions to the equation for the cumulative density and prove convergence of approximate solutions to weak solutions of the…

A posteriori error estimates and space-adaptive mesh refinements for time-dependent scattering problems
Th\'eophile Chaumont-Frelet, Heiko Gimperlein, Ignacio Labarca-Figueroa, J\"org Nick
https://arxiv.org/abs/2509.04217

A posteriori error estimates and space-adaptive mesh refinements for time-dependent scattering problems
This work studies a posteriori error estimates and their use for time-dependent acoustic scattering problems, formulated as a time-dependent boundary integral equation based on a single-layer ansatz. The integral equation is discretized by the convolution quadrature method in time and by boundary elements in space. We prove the reliability of an error estimator of residual type and study the resulting space-adaptive mesh refinements. Moreover, we present a simple modification of the convolution…

Subadditivity of the log-Sobolev constant on convolutions
Thomas A. Courtade, Edric Wang
https://arxiv.org/abs/2508.19648 https://arxiv.org/pdf/2508.19648

Subadditivity of the log-Sobolev constant on convolutions
We present a general subadditivity inequality for log-Sobolev constants of convolution measures. As a corollary, we show that the log-Sobolev constant is monotone along the sequence of standardized convolutions in the central limit theorem.

Kolmogorov equations for stochastic Volterra processes with singular kernels
Ioannis Gasteratos, Alexandre Pannier
https://arxiv.org/abs/2509.21608 https://

Kolmogorov equations for stochastic Volterra processes with singular kernels
We associate backward and forward Kolmogorov equations to a class of fully nonlinear Stochastic Volterra Equations (SVEs) with convolution kernels $K$ that are singular at the origin. Working on a carefully chosen Hilbert space $\mathcal{H}_1$, we rigorously establish a link between solutions of SVEs and Markovian mild solutions of a Stochastic Partial Differential Equation (SPDE) of transport-type. Then, we obtain two novel Itô formulae for functionals of mild solutions and, as a byproduct, s…

A Scalable Distributed Framework for Multimodal GigaVoxel Image Registration
Rohit Jena, Vedant Zope, Pratik Chaudhari, James C. Gee
https://arxiv.org/abs/2509.25044 https://

A Scalable Distributed Framework for Multimodal GigaVoxel Image Registration
In this work, we propose FFDP, a set of IO-aware non-GEMM fused kernels supplemented with a distributed framework for image registration at unprecedented scales. Image registration is an inverse problem fundamental to biomedical and life sciences, but algorithms have not scaled in tandem with image acquisition capabilities. Our framework complements existing model parallelism techniques proposed for large-scale transformer training by optimizing non-GEMM bottlenecks and enabling convolution-awa…

A Convolutional Hierarchical Deep-learning Neural Network (C-HiDeNN) Framework for Non-linear Finite Element/Meshfree Analysis
Yingjian Liu, Monish Yadav Pabbala, Jiachen Guo, Chanwook Park, Gino Domel, Wing Kam Liu, Dong Qian
https://arxiv.org/abs/2509.02435

A Convolutional Hierarchical Deep-learning Neural Network (C-HiDeNN) Framework for Non-linear Finite Element/Meshfree Analysis
We present a framework for the Convolutional Hierarchical Deep Neural Network (C-HiDeNN) tailored for nonlinear finite element and meshfree analysis. Building upon the structured foundation of HiDeNN, which includes the evaluation of shape function derivatives, adaptivity, and material derivatives, C-HiDeNN introduces a convolution operator to enhance the HiDeNN approximation. A distinctive feature of C-HiDeNN is its expanded set of optimization parameters, such as the polynomial order 'p,' dil…

On nonlinear elliptic problems with Hardy-Littlewood-Sobolev critical exponent and Sobolev-Hardy critical exponent
Guangze Gu, Aleks Jevnikar
https://arxiv.org/abs/2509.15806 ht…

On nonlinear elliptic problems with Hardy-Littlewood-Sobolev critical exponent and Sobolev-Hardy critical exponent
In this paper we use variational methods to establish the existence of solutions for a class of nonlinear elliptic problems involving a combined convolution-type and Hardy nonlinearity with subcritical and critical growth.

Functional Liftings of Restricted Geometric Inequalities
Andreas Malliaris, James Melbourne, Cyril Roberto, Michael Roysdon
https://arxiv.org/abs/2508.15247 https://

Functional Liftings of Restricted Geometric Inequalities
We investigate what we term ``generalized sup-convolutions". We show that functional inequalities that enjoy an interpretation as sup-convolution inequalities can be deduced from the special case of indicator functions corresponding to a geometric inequality. As consequences we derive a Borell-Brascamp Lieb inequality for the Gaussian Brunn-Minkowski inequality and give a functional analog of the log-Brunn-Minkowski conjecture. We also show that Barthe's reverse Brascamp-Lieb inequality is equi…

Asymptotic root distribution of polynomials under repeated polar differentiation
Daniel Perales, Zhiyuan Yang
https://arxiv.org/abs/2508.18575 https://arxi…

Asymptotic root distribution of polynomials under repeated polar differentiation
Given a sequence of real rooted polynomials $\{p_n\}_{n\geq 1}$ with a fixed asymptotic root distribution, we study the asymptotic root distribution of the repeated polar derivatives of this sequence. This limiting distribution can be seen as the result of fractional free convolution and pushforward maps along Möbius transforms for distributions. This new family of operations on measures forms a semigroup and satisfy some other nice properties. Using the fact that polar derivatives commute wit…

An Efficient Finite Element Method for Multi-dimensional Nonlocal Laplacian on Uniform Grids
Changtao Sheng, Huiyuan Li, Huifang Yuan, Li-Lian Wang
https://arxiv.org/abs/2509.24809

An Efficient Finite Element Method for Multi-dimensional Nonlocal Laplacian on Uniform Grids
Computing the stiffness matrix for the finite element discretization of the nonlocal Laplacian on unstructured meshes is difficult, because the operator is nonlocal and can even be singular. In this paper, we focus on the $C^0$-piecewise linear finite element method (FEM) for the nonlocal Laplacian on uniform grids within a $d$-dimensional rectangular domain. By leveraging the connection between FE bases and B-splines (having attractive convolution properties), we can reduce the involved $2d$-d…

A Lightweight Group Multiscale Bidirectional Interactive Network for Real-Time Steel Surface Defect Detection
Yong Zhang, Cunjian Chen, Qiang Gao, Yi Wang, Bin Fang
https://arxiv.org/abs/2508.16397

A Lightweight Group Multiscale Bidirectional Interactive Network for Real-Time Steel Surface Defect Detection
Real-time surface defect detection is critical for maintaining product quality and production efficiency in the steel manufacturing industry. Despite promising accuracy, existing deep learning methods often suffer from high computational complexity and slow inference speeds, which limit their deployment in resource-constrained industrial environments. Recent lightweight approaches adopt multibranch architectures based on depthwise separable convolution (DSConv) to capture multiscale contextual …

Two ADI compact difference methods for variable-exponent diffusion wave equations
Hao Zhang, Kexin Li, Wenlin Qiu
https://arxiv.org/abs/2509.21316 https://…

Two ADI compact difference methods for variable-exponent diffusion wave equations
In this work, we study two-dimensional diffusion-wave equations with variable exponent, modeling mechanical diffusive wave propagation in viscoelastic media with spatially varying properties. We first transform the diffusion-wave model into an equivalent form via the convolution method. Two time discretization strategies are then applied to approximate each term in the transformed equation, yielding two fully discrete schemes based on a spatial compact finite difference method. To reduce comput…