Tootfinder

Solar Cycle Variations in Meridional Flows and Rotational Shear within the Sun's Near-surface Shear Layer: #solar rotation: https://www.nature.com/articles/d44151-025-00101-6 - scientists uncover how deep plasma flows reshape the Sun’s rotation and hint at mysteries below its surface.

A Study of Hybrid and Evolutionary Metaheuristics for Single Hidden Layer Feedforward Neural Network Architecture
Gautam Siddharth Kashyap, Md Tabrez Nafis, Samar Wazir
https://arxiv.org/abs/2506.15737

A Study of Hybrid and Evolutionary Metaheuristics for Single Hidden Layer Feedforward Neural Network Architecture
Training Artificial Neural Networks (ANNs) with Stochastic Gradient Descent (SGD) frequently encounters difficulties, including substantial computing expense and the risk of converging to local optima, attributable to its dependence on partial weight gradients. Therefore, this work investigates Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) - two population-based Metaheuristic Optimizers (MHOs) - as alternatives to SGD to mitigate these constraints. A hybrid PSO-SGD strategy is …

Rank Inspired Neural Network for solving linear partial differential equations
Wentao Peng, Yunqing Huang, Nianyu Yi
https://arxiv.org/abs/2506.17654 https…

Rank Inspired Neural Network for solving linear partial differential equations
This paper proposes a rank inspired neural network (RINN) to tackle the initialization sensitivity issue of physics informed extreme learning machines (PIELM) when numerically solving partial differential equations (PDEs). Unlike PIELM which randomly initializes the parameters of its hidden layers, RINN incorporates a preconditioning stage. In this stage, covariance-driven regularization is employed to optimize the orthogonality of the basis functions generated by the last hidden layer. The key…

KKT-based optimality conditions for neural network approximation
Vinesha Peiris, Nadezda Sukhorukova, Julien Ugon
https://arxiv.org/abs/2506.17305 https://…

KKT-based optimality conditions for neural network approximation
In this paper, we obtain necessary optimality conditions for neural network approximation. We consider neural networks in Manhattan ($l_1$ norm) and Chebyshev ($\max$ norm). The optimality conditions are based on neural networks with at most one hidden layer. We reformulate nonsmooth unconstrained optimisation problems as larger dimension constrained problems with smooth objective functions and constraints. Then we use KKT conditions to develop the necessary conditions and present the optimalit…

Widely Linear Augmented Extreme Learning Machine Based Impairments Compensation for Satellite Communications
Yang Luo, Arunprakash Jayaprakash, Gaojie Chen, Chong Huang, Qu Luo, De Mi, Pei Xiao
https://arxiv.org/abs/2506.14557

Widely Linear Augmented Extreme Learning Machine Based Impairments Compensation for Satellite Communications
Satellite communications are crucial for the evolution beyond fifth-generation networks. However, the dynamic nature of satellite channels and their inherent impairments present significant challenges. In this paper, a novel post-compensation scheme that combines the complex-valued extreme learning machine with augmented hidden layer (CELMAH) architecture and widely linear processing (WLP) is developed to address these issues by exploiting signal impropriety in satellite communications. Althoug…

Regional, Lattice and Logical Representations of Neural Networks
Sandro Preto (Federal University of ABC, Brazil), Marcelo Finger (University of Sao Paulo, Brazil)
https://arxiv.org/abs/2506.05834

Regional, Lattice and Logical Representations of Neural Networks
A possible path to the interpretability of neural networks is to (approximately) represent them in the regional format of piecewise linear functions, where regions of inputs are associated to linear functions computing the network outputs. We present an algorithm for the translation of feedforward neural networks with ReLU activation functions in hidden layers and truncated identity activation functions in the output layer. We also empirically investigate the complexity of regional representati…

This https://arxiv.org/abs/2505.09551 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Fast Learning in Quantitative Finance with Extreme Learning Machine
This paper demonstrates that a broad class of problems in quantitative finance, including those previously addressed using deep neural networks, can be efficiently solved using single-layer neural networks without iterative gradient-based training, namely extreme learning machine (ELM). ELM utilizes a single-layer network with randomly initialized hidden nodes and analytically computed output weights obtained via convex optimization, enabling rapid training and inference. Both supervised and un…

This https://arxiv.org/abs/2403.05809 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Shallow ReLU neural networks and finite elements
We point out that (continuous or discontinuous) piecewise linear functions on a convex polytope mesh can be represented by two-hidden-layer ReLU neural networks in a weak sense. In addition, the numbers of neurons of the two hidden layers required to weakly represent are accurately given based on the numbers of polytopes and hyperplanes involved in this mesh. The results naturally hold for constant and linear finite element functions. Such weak representation establishes a bridge between shallo…