Tootfinder

Physics-Informed Deep Contrast Source Inversion: A Unified Framework for Inverse Scattering Problems
Haoran Sun, Daoqi Liu, Hongyu Zhou, Maokun Li, Shenheng Xu, Fan Yang
https://arxiv.org/abs/2508.10555

Physics-Informed Deep Contrast Source Inversion: A Unified Framework for Inverse Scattering Problems
Inverse scattering problems are critical in electromagnetic imaging and medical diagnostics but are challenged by their nonlinearity and diverse measurement scenarios. This paper proposes a physics-informed deep contrast source inversion framework (DeepCSI) for fast and accurate medium reconstruction across various measurement conditions. Inspired by contrast source inversion (CSI) and neural operator methods, a residual multilayer perceptron (ResMLP) is employed to model current distributions …

I have decided to suffer, and hence made a genetic algorithm that through linear algebra generates colours palettes to be automatically scored by a small perceptron that will be live trained by a human who will pick a score for a sample of palettes, all to try and generate automatically colour palettes that an artist could find appealing for the sole goal to figure out if colour pleasantness is evaluable and/or reproducible by linear algebra
It is 1:30 AM and I have a full backend, 4 t…

All for One: LLMs Solve Mental Math at the Last Token With Information Transferred From Other Tokens
Siddarth Mamidanna, Daking Rai, Ziyu Yao, Yilun Zhou
https://arxiv.org/abs/2509.09650

All for One: LLMs Solve Mental Math at the Last Token With Information Transferred From Other Tokens
Large language models (LLMs) demonstrate proficiency across numerous computational tasks, yet their inner workings remain unclear. In theory, the combination of causal self-attention and multilayer perceptron layers allows every token to access and compute information based on all preceding tokens. In practice, to what extent are such operations present? In this paper, on mental math tasks (i.e., direct math calculation via next-token prediction without explicit reasoning), we investigate this …

Node Position Estimation in Diffusion-Based Molecular Communications Using Multi-Layer Perceptron
Sangjun Hwang, Chan-Byoung Chae
https://arxiv.org/abs/2509.07441 https://

Node Position Estimation in Diffusion-Based Molecular Communications Using Multi-Layer Perceptron
This paper proposes a method for accurately estimating the relative position between two nodes with unknown locations in a diffusion-based molecular communication environment. A specialized node structure is designed, combining a central absorbing receiver with multiple transmitters placed at predefined spherical coordinates. Pilot molecules are released, and their absorption time and concentration are measured. By partitioning the spherical coordinate space, these spatially distinct measuremen…

Heart Disease Prediction: A Comparative Study of Optimisers Performance in Deep Neural Networks
Chisom Chibuike, Adeyinka Ogunsanya
https://arxiv.org/abs/2509.08499 https://

Heart Disease Prediction: A Comparative Study of Optimisers Performance in Deep Neural Networks
Optimization has been an important factor and topic of interest in training deep learning models, yet less attention has been given to how we select the optimizers we use to train these models. Hence, there is a need to dive deeper into how we select the optimizers we use for training and the metrics that determine this selection. In this work, we compare the performance of 10 different optimizers in training a simple Multi-layer Perceptron model using a heart disease dataset from Kaggle. We se…

Network-Specific Models for Multimodal Brain Response Prediction
Andrea Corsico, Giorgia Rigamonti, Simone Zini, Luigi Celona, Paolo Napoletano
https://arxiv.org/abs/2508.06499 …

Network-Specific Models for Multimodal Brain Response Prediction
In this work, we present a network-specific approach for predicting brain responses to complex multimodal movies, leveraging the Yeo 7-network parcellation of the Schaefer atlas. Rather than treating the brain as a homogeneous system, we grouped the seven functional networks into four clusters and trained separate multi-subject, multi-layer perceptron (MLP) models for each. This architecture supports cluster-specific optimization and adaptive memory modeling, allowing each model to adjust tempo…

Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP
Aueaphum Aueawatthanaphisut, Nyi Wunna Tun
https://arxiv.org/abs/2510.06010

Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP
The comparative evaluation between classical and quantum reinforcement learning (QRL) paradigms was conducted to investigate their convergence behavior, robustness under observational noise, and computational efficiency in a benchmark control environment. The study employed a multilayer perceptron (MLP) agent as a classical baseline and a parameterized variational quantum circuit (VQC) as a quantum counterpart, both trained on the CartPole-v1 environment over 500 episodes. Empirical results dem…

Identification of Strongly Lensed Gravitational Wave Events Using Squeeze-and-Excitation Multilayer Perceptron Data-efficient Image Transformer
Dejiang Li, Tonghua Liu, Ao Liu, Cuihong Wen, Jieci Wang, Kai Liao, Jiaxing Cui
https://arxiv.org/abs/2508.19311

Identification of Strongly Lensed Gravitational Wave Events Using Squeeze-and-Excitation Multilayer Perceptron Data-efficient Image Transformer
With the advancement of third-generation gravitational wave detectors, the identification of strongly lensed gravitational wave (GW) events is expected to play an increasingly vital role in cosmology and fundamental physics. However, traditional Bayesian inference methods suffer from combinatorial computational overhead as the number of events grows, making real-time analysis infeasible. To address this, we propose a deep learning model named Squeeze-and-Excitation Multilayer Perceptron Data-ef…

Algebraic Approach to Ridge-Regularized Mean Squared Error Minimization in Minimal ReLU Neural Network
Ryoya Fukasaku, Yutaro Kabata, Akifumi Okuno
https://arxiv.org/abs/2508.17783

Algebraic Approach to Ridge-Regularized Mean Squared Error Minimization in Minimal ReLU Neural Network
This paper investigates a perceptron, a simple neural network model, with ReLU activation and a ridge-regularized mean squared error (RR-MSE). Our approach leverages the fact that the RR-MSE for ReLU perceptron is piecewise polynomial, enabling a systematic analysis using tools from computational algebra. In particular, we develop a Divide-Enumerate-Merge strategy that exhaustively enumerates all local minima of the RR-MSE. By virtue of the algebraic formulation, our approach can identify not o…

On the sensitivity of different galaxy properties to warm dark matter
Bel\'en Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
https://arxiv.org/abs/2510.05037

On the sensitivity of different galaxy properties to warm dark matter
We study the impact of warm dark matter (WDM) particle mass on galaxy properties using 1,024 state-of-the-art cosmological hydrodynamical simulations from the DREAMS project. We begin by using a Multilayer Perceptron (MLP) coupled with a normalizing flow to explore global statistical descriptors of galaxy populations, such as the mean, standard deviation, and histograms of 14 galaxy properties. We find that subhalo gas mass is the most informative feature for constraining the WDM mass, achievin…

Crosslisted article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representation
Chuanxiang Yang, Yuanfeng Zhou, Guangshun Wei, Siyu Ren, Yuan Liu, Junhui Hou, Wenping Wang

Multi-Layer Perceptron-Based Relay Node Selection for Next-Generation Intelligent Delay-Tolerant Networks
Zhekun Huang, Milena Radenkovic
https://arxiv.org/abs/2509.11239 https:…

Multi-Layer Perceptron-Based Relay Node Selection for Next-Generation Intelligent Delay-Tolerant Networks
Delay Tolerant Networks (DTNs) are critical for emergency communication in highly dynamic and challenging scenarios characterized by intermittent connectivity, frequent disruptions, and unpredictable node mobility. While some protocols are widely adopted for simplicity and low overhead, their static replication strategy lacks the ability to adaptively distinguish high-quality relay nodes, often leading to inefficient and suboptimal message dissemination. To address this challenge, we propose a …

Crosslisted article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
[1/1]:
- T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representation
Chuanxiang Yang, Yuanfeng Zhou, Guangshun Wei, Siyu Ren, Yuan Liu, Junhui Hou, Wenping Wang

First Experience with Real-Time Control Using Simulated VQC-Based Quantum Policies
Yize Sun, Mohamad Hagog, Marc Weber, Daniel Hein, Steffen Udluft, Volker Tresp, Yunpu Ma
https://arxiv.org/abs/2508.01690

First Experience with Real-Time Control Using Simulated VQC-Based Quantum Policies
This paper investigates the integration of quantum computing into offline reinforcement learning and the deployment of the resulting quantum policy in a real-time control hardware realization of the cart-pole system. Variational Quantum Circuits (VQCs) are used to represent the policy. Classical model-based offline policy search was applied, in which a pure VQC with trainable input-output weights is used as a policy network instead of a classical multilayer perceptron. The goal is to evaluate t…

Efficient Online Large-Margin Classification via Dual Certificates
Nam Ho-Nguyen, Fatma K{\i}l{\i}n\c{c}-Karzan, Ellie Nguyen, Lingqing Shen
https://arxiv.org/abs/2509.19670 htt…

Efficient Online Large-Margin Classification via Dual Certificates
Online classification is a central problem in optimization, statistical learning and data science. Classical algorithms such as the perceptron offer efficient updates and finite mistake guarantees on linearly separable data, but they do not exploit the underlying geometric structure of the classification problem. We study the offline maximum margin problem through its dual formulation and use the resulting geometric insights to design a principled and efficient algorithm for the online setting.…

Sinusoidal Approximation Theorem for Kolmogorov-Arnold Networks
Sergei Gleyzer, Hanh Nguyen, Dinesh P. Ramakrishnan, Eric A. F. Reinhardt
https://arxiv.org/abs/2508.00247 https:…

Sinusoidal Approximation Theorem for Kolmogorov-Arnold Networks
The Kolmogorov-Arnold representation theorem states that any continuous multivariable function can be exactly represented as a finite superposition of continuous single variable functions. Subsequent simplifications of this representation involve expressing these functions as parameterized sums of a smaller number of unique monotonic functions. These developments led to the proof of the universal approximation capabilities of multilayer perceptron networks with sigmoidal activations, forming th…

{\L}ukasiewicz Logic with Actions for Neural Networks training
Ioana Leu\c{s}tean (University of Bucharest), Bogdan Macovei (University of Bucharest)
https://arxiv.org/abs/2509.13020

Łukasiewicz Logic with Actions for Neural Networks training
Based on the already known connection between multilayer perceptrons and Lukasiewicz logic with rational coefficients, we take a step forward in analyzing its training process using a three-sorted hybrid modal logic: a multilayer perceptron is a logical formula; the actions of the training process are modal operators; the training process is a sequence of logical deductions. Using the proof assistant and the programming language Lean 4, the algorithmic implementation of the training process is …

(Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
Luca Bergamin, Roberto Confalonieri, Fabio Aiolli
https://arxiv.org/abs/2509.22384

(Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
Deep neural networks are widely used in practical applications of AI, however, their inner structure and complexity made them generally not easily interpretable. Model transparency and interpretability are key requirements for multiple scenarios where high performance is not enough to adopt the proposed solution. In this work, a differentiable approximation of $L_0$ regularization is adapted into a logic-based neural network, the Multi-layer Logical Perceptron (MLLP), to study its efficacy in r…

On the solution of Euclidean path integrals with neural networks
Gabor Balassa
https://arxiv.org/abs/2509.16953 https://arxiv.org/pdf/2509.16953

On the solution of Euclidean path integrals with neural networks
This paper proposes a numerical method using neural networks to solve the path integral problem in quantum mechanics for arbitrary potentials. The method is based on a radial basis function expansion of the interaction term that appears in the Euclidean path integral formalism. By constructing a corresponding multi-layered perceptron-type neural network with exponential nonlinearities in the hidden layer, the original path integral can be approximated by a linear combination of Gaussian path in…

Data-driven mean-field within whole-brain models
Martin Breyton, Viktor Sip, Marmaduke Woodman, Meysam Hashemi, Spase Petkoski, Viktor Jirsa
https://arxiv.org/abs/2509.02799 htt…

Data-driven mean-field within whole-brain models
Mean-field models provide a link between microscopic neuronal activity and macroscopic brain dynamics. Their derivation depends on simplifying assumptions, such as all-to-all connectivity, limiting their biological realism. To overcome this, we introduce a data-driven framework in which a multi-layer perceptron (MLP) learns the macroscopic dynamics directly from simulations of a network of spiking neurons. The network connection probability serves here as a new parameter, inaccessible to purely…

Physics-Guided Neural Networks for Constructing Nucleon-Nucleon Inverse Potentials
Ayushi Awasthi, Anil Khachi, M. R. Ganesh Kumar, O. S. K. S. Sastri
https://arxiv.org/abs/2509.01539

Physics-Guided Neural Networks for Constructing Nucleon-Nucleon Inverse Potentials
We propose a physics-guided neural network (PGNN) framework for constructing nucleon-nucleon inverse potentials based on inverse scattering theory. The framework integrates the Phase Function Method (PFM) with a two-stage supervised multi-layer perceptron (MLP) model to extract the optimal parameters of the Malfliet-Tjon (MT) potential from sparse phase-shift data. A synthetic dataset of phase shifts is generated by solving the phase equation for angular momentum $\ell$ = 0, using the fifth-ord…

Deep Learning for Short Term Equity Trend Forecasting: A Behavior Driven Multi Factor Approach
Yuqi Luan
https://arxiv.org/abs/2508.14656 https://arxiv.org…

Deep Learning for Short Term Equity Trend Forecasting: A Behavior Driven Multi Factor Approach
This study proposes a behaviorally-informed multi-factor stock selection framework that integrates short-cycle technical alpha signals with deep learning. We design a dual-task multilayer perceptron (MLP) that jointly predicts five-day future returns and directional price movements, thereby capturing nonlinear market behaviors such as volume-price divergence, momentum-driven herding, and bottom reversals. The model is trained on 40 carefully constructed factors derived from price-volume pattern…

Learning Internal Biological Neuron Parameters and Complexity-Based Encoding for Improved Spiking Neural Networks Performance
Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska
https://arxiv.org/abs/2508.11674

Learning Internal Biological Neuron Parameters and Complexity-Based Encoding for Improved Spiking Neural Networks Performance
This study introduces a novel approach by replacing the traditional perceptron neuron model with a biologically inspired probabilistic meta neuron, where the internal neuron parameters are jointly learned, leading to improved classification accuracy of spiking neural networks (SNNs). To validate this innovation, we implement and compare two SNN architectures: one based on standard leaky integrate-and-fire (LIF) neurons and another utilizing the proposed probabilistic meta neuron model. As a sec…

Machine Learning based Radio Environment Map Estimation for Indoor Visible Light Communication
Helena Serpi (Tanya), Christina (Tanya), Politi
https://arxiv.org/abs/2507.19149

Machine Learning based Radio Environment Map Estimation for Indoor Visible Light Communication
An innovative method for radio map estimation in optical wireless communications is proposed that is based on Machine Learning rather than simulation techniques. Multi-Layer Perceptron (MLP) representation of indoor Visible Light Communication (VLC) systems is suggested, and signal propagation is estimated. The simulation and performance predictions are accurate, fast and require a reduced set of training sample size with respect to other counterparts, making this solution very suitable for rea…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Nathan He, Cody Chen
https://arxiv.org/abs/2509.12287 https://arxi…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Existing deep learning models for chest radiology often neglect patient metadata, limiting diagnostic accuracy and fairness. To bridge this gap, we introduce MetaCheX, a novel multimodal framework that integrates chest X-ray images with structured patient metadata to replicate clinical decision-making. Our approach combines a convolutional neural network (CNN) backbone with metadata processed by a multilayer perceptron through a shared classifier. Evaluated on the CheXpert Plus dataset, MetaChe…

PreSem-Surf: RGB-D Surface Reconstruction with Progressive Semantic Modeling and SG-MLP Pre-Rendering Mechanism
Yuyan Ye, Hang Xu, Yanghang Huang, Jiali Huang, Qian Weng
https://arxiv.org/abs/2508.13228

PreSem-Surf: RGB-D Surface Reconstruction with Progressive Semantic Modeling and SG-MLP Pre-Rendering Mechanism
This paper proposes PreSem-Surf, an optimized method based on the Neural Radiance Field (NeRF) framework, capable of reconstructing high-quality scene surfaces from RGB-D sequences in a short time. The method integrates RGB, depth, and semantic information to improve reconstruction performance. Specifically, a novel SG-MLP sampling structure combined with PR-MLP (Preconditioning Multilayer Perceptron) is introduced for voxel pre-rendering, allowing the model to capture scene-related information…