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openalexZenodo (CERN European Organization for Nuclear Research)Cited by 0

Trained neural network potential models for Algorithmic first-principles reaction discovery uncovers overlooked cubane transformations

Wataru Matsuoka, Taihei Oki, Kosaku Tanaka, Ren Yamada, Ruben Staub, Alexandre Varnek, Tsuyoshi Mita, Yu Harabuchi, Satoru Iwata, Satoshi Maeda

This dataset contains the neural network potential (NNP) models used in the associated manuscript, including standalone executables and the Python interface for Au-, Ag-, and Cu-catalyzed systems. The models can be used together with the VLA-PRO package to reproduce the calculations reported in the manuscript. Installation instructions, example calculations, and expected outputs are provided in the accompanying README files.

Also available via: European Organization for Nuclear Research

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

Electrical resistivity tomography surveys, trained physics-informed neural network models and code for amortized ERT inversion along Route Regionale 707, Moroccan Middle Atlas

Rajae Ajana

This deposit contains the field data, synthetic training datasets, trained network weights and analysis code supporting the article "Physics-informed neural network inversion of electrical resistivity tomography data: amortized optimization with field validation in the Moroccan M…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25

Quality of Service (QoS) Optimization in 5G/6G Networks Using Neural Networks

Charis E Shiny, S Annapurna, C Lakshana, Anusha Fakirappa Bogur, S Ramesh, G R Naik

Abstract: 5G is rolled out and next generation 6G networks are also being developed, ultra-low latency (URLL) communication as a standard is critical in supporting the plethora of applications, spanning autonomous vehicles, immersive extended reality experience, etc. However, tra…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

A Theoretically Grounded Spiking Neural Network Architecture for Real-Time Intracortical Signal Processing: Epistemological Foundations, Mathematical Guarantees, Computationally Verified Results, and Falsifiable Predictions for Closed-Loop Brain–Computer Interfaces

Sami Shibah

Epistemological position. This work adopts a critical-rationalist stance (Popper, 1959): every theoretical claim is stated as a conjecture subject to empirical falsification, with quantitative rejection thresholds fixed a priori. Every mathematical guarantee is derived from expli…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25

Leakage-Safe Evaluation of Sensor-Failure Robustness in Dynamic Gas Mixture Quantification

Bakti Dwi Waluyo, Muhammad Aulia Rahman Sembiring

This repository contains the complete execution pipeline for the study: "Leakage-Safe Evaluation of Stochastic Channel Masking for Sensor-Failure Robustness in Dynamic Gas Mixture Quantification." The code provides an end-to-end reproducible workflow for processing the UCI Gas Se…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

AI-Powered Fault Detection and Interpretation: From Neural Networks to Ready-to-Use Fault Surfaces

Alexander Shcherbina, Petr Popov, Ruslan Peisakhov, Yulia Sherman, Alex Berkovich

We present a comprehensive automated solution for 3D seismic fault detection and interpretation that combines deep learning with advanced geometric post-processing. The method integrates a 3D U-Net neural network trained on synthetic data with normalized distance function targets…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

Graph Neural Networks for Predicting Solvability of Finite Groups

Tal Weissblat

We present a Graph Neural Network (GNN) framework for the classification of finite groups according to their solvability. Using undirected Cayley graph representations, the proposed framework learns to distinguish solvable and non-solvable groups directly from structural graph in…

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