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4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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When Integral Meets Decomposition: A Signal-Level Self-Supervised Feature Decompose Paradigm for Multi-Modal Image Fusion
Sep 30, 2026Zeyu Wang, Jiayu Wang, Haiyu Song +1Multimodal Fusion Visible Image Fusion
College of Computer Science and Engineering, Dalian Minzu University, Dalian, China · School of Artificial Intelligence and Robotics, Hunan University, Hunan, China · Department of Automation, Tsinghua University, Beijing, China
Two-Sample Testing for Inhomogeneous Random Graphs in Non-Integral $L_r$ Norms
Sep 27, 2026Soham DanInhomogeneous Random Graphs Two-Sample Testing
Contact-Stable Deformable Tissue Simulation Using Implicit Integration and Live-Pose Grasp Constraints for Laparoscopic Surgery Robot Policy Evaluation
Sep 22, 2026Juahn Oh, Dongho Yee, Jinseok Lee +9Surgical Robots Robotic Grasping
Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Republic of Korea. · Rosota Inc., Seoul, Republic of Korea. · Eulji University College of Medicine, Daejeon, Republic of Korea. +6
Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection
Sep 11, 2026Naveenraj Kamalakannan, Sri Ram Macharla, M Kanimozhi +1Chest X-Ray Respiratory Sound Classification
a – The Construct, Barcelona, Spain · b – Involgixs Inc, USA · c - School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India
Understanding Differentiable Embeddings Through Differential and Integral Geometry
Aug 7, 2026Xinyu Zhang, Klaus MuellerData Manifold Behavioral Embeddings
Stony Brook University · Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
Tangent classes of matroids and wonderful compactifications
Jul 7, 2026Ronnie Cheng, Shurui Liu, Guoxiong GaoHilbert Series Tangent Space
Computing the Integral R2 Indicator by Perspective Mapping and Box Decomposition
Jun 29, 2026Michael T. M. EmmerichIntegral Multi-Objective Optimization
Faculty of Information Technology, University of Jyväskylä, Finland
Three-Objective Integral R2 Subset Selection: NP-Hardness and Submodular Approximation
Jun 25, 2026Michael T. M. EmmerichSubmodular Functions Multi-Objective Optimization
Quantum-classical physics-informed Kolmogorov-Arnold networks for PDEs
Jun 18, 2026Xiang Rao, Yuxuan ShenParametric Physics-Informed Neural Network Partial Differential Equations
1. School of Petroleum Engineering, Yangtze University, Wuhan 430100, China · 2. School of Computer Science, Yangtze University, Jingzhou 434023, China · 3. State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization (Yangtze University), Wuhan 430100, China +2
A nonparametric two-sample test using a parametric integral probability metric
Jun 15, 2026Yuha Park, Yongdai KimTwo-Sample Testing Exponential Family
Department of Mathematics, University of Hamburg, Hamburg, Germany. · Department of Statistics, Seoul National University, Seoul, Republic of Korea.
Integral Formulation of QENDy for Robust Nonlinear System Identification
Jun 10, 2026Nikhil Saran, Sushant Pokhriyal, Stefan Klus +2System Identification Nonlinear Dynamics
Department of Mathematics and Statistics at the University of South Florida · Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, India · School of Mathematical & Computer Sciences at the Heriot–Watt University, Edinburgh +1
Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seeding
Jun 9, 2026Justin Berman, Francois Charton, Andres Luna +2Unified Path-Integral Update Integral
Leinweber Institute for Theoretical Physics, Randall Laboratory of Physics, University of Michigan, Ann Arbor, 450 Church St, Ann Arbor, MI 48109-1040, USA · Axiom Math, 124 University Avenue, Palo Alto, California, 94301, United States · CERMICS, Ecole Nationale des Ponts et Chauss´ees, 8 Avenue Blaise Pascal, 77420, Champs sur Marne +3
A holomorphic neural network framework for 3D boundary value problems governed by harmonic potentials
May 29, 2026Enrico Ballini, Allan Peter Engsig-Karup, Tito AndriolloVariational Formulation Neural Network
Department of Mechanical and Production Engineering, Aarhus University, Aarhus, Denmark · Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
Domain-Informed Representation for Evolutionary Sieving in Integral and Module Lattices
May 27, 2026Ahmad Tashfeen, Qi ChengDiffie-Hellman Post-Quantum Cryptography
University of Oklahoma, Norman OK 73019, USA
Learning First Integrals via Backward-Generated Data and Guided Reinforcement Learning
May 20, 2026Jingfeng Zhong, Zhengxiang Liu, Zhijie Wang +1Integral Functional Code Solvers
Shanghai Jiao Tong University China
CRADIPOR: Crash Dispersion Predictor
Apr 30, 2026Edgar Chaillou, Sebastian Rodriguez, Yves Tourbier +1Crashes Dispersion
PIMM, Arts et Métiers Institute of Technology, Paris, France · Renault Group, Guyancourt, France
Explicit integral representations and quantitative bounds for two-layer ReLU networks
Apr 25, 2026Anthony LeeRectified Linear Unit Networks Rectified Linear Unit
School of Mathematics, University of Bristol
Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions
Apr 23, 2026Di Wu, Ling Liang, Haizhao YangBayesian Experimental Design Integral
Department of Mathematics The University of Tennessee, Knoxville TN 37916, USA · Department of Mathematics and Department of Computer Science University of Maryland, College Park MD 20742, USA
A Sugeno Integral View of Binarized Neural Network Inference
Apr 20, 2026Ismaïl Baaj, Henri PradeNeural Network Inference Neural Network
LEMMA, Paris-Panthéon-Assas University · IRIT - Toulouse University
Quantification of Credal Uncertainty: A Distance-Based Approach
Mar 28, 2026Xabier Gonzalez-Garcia, Siu Lun Chau, Julian Rodemann +6Uncertainty Multiclass Classification
Dept. of Statistics, CS & Mathematics, Public Univ. of Navarre · EPIC Lab, Nanyang Technological Univ. Singapore · Rational Intelligence Lab, CISPA Helmholtz Center for Information Security +7
Bayesian Quadrature
Feb 18, 2026Maren Mahsereci, Toni KarvonenBayesian Quadrature Integral
Yahoo Research · School of Engineering Sciences Lappeenranta–Lahti University of Technology LUT
Methods to integrate multinormals and compute classification measures
Dec 23, 2020Abhranil Das, Wilson S GeislerDiscriminative Norm
Department of Physics, The University of Texas at Austin · Center for Perceptual Systems, The University of Texas at Austin · Center for Theoretical and Computational Neuroscience, The University of Texas at Austin +1

Sep 30, 2026, cs.CVJ/K move · Enter open · S save

When Integral Meets Decomposition: A Signal-Level Self-Supervised Feature Decompose Paradigm for Multi-Modal Image Fusion

Zeyu Wang, Jiayu Wang, Haiyu Song, Haoran Duan

College of Computer Science and Engineering, Dalian Minzu University, Dalian, China · School of Artificial Intelligence and Robotics, Hunan University, Hunan, China · Department of Automation, Tsinghua University, Beijing, China

Multimodal Fusion Visible Image Fusion Modalities Integral Structural Constraints

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Multimodal image fusion (MMIF) aims to integrate complementary information from different modalities into a high-quality fused image and support downstream tasks. Recently, feature decomposition has become an important paradigm by separating source images into common and modality-specific unique features. However, existing methods lack clear supervision because ground-truth (GT) decomposition feature maps are unavailable. They usually combine multiple image-level metrics as losses, which are inherently incomplete and may conflict since each pixel couples attributes such as texture, edge, and contour. To address this, we propose a 1D signal-level self-supervised feature decomposition paradigm. Our core insight is to reformulate feature decomposition from unclear 2D image-level supervision into an integral-driven 1D signal-level optimization problem. This objective-level reformulation uses the 1D signal form to compute the integral constraint. The decomposer is optimized by the integral area between common and original signals, enabling more stable optimization with a clear optimization objective. Our model follows a two-stage SSL framework. Stage I designs dual pretext tasks for integral-driven decomposition at the signal level and structure-preserving reconstruction at the image level. Stage II fuses unique features and combines them with common features to reconstruct the fused image. Experiments on representative MMIF tasks show state-of-the-art (SOTA) performance. Code: github.com/Wangjiayu0512/SIDFusion.

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