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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26Cited by 0

PredANN++: Code for Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity

Shogo Noguchi, Taketo Akama, Tai Nakamura, Shun Minamikawa, Natalia Polouliakh

Source code for the PredANN++ pipeline for EEG-based music identification using acoustic, surprisal, and entropy teacher representations.

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

A Technical Note on a Construction Method for Neural Networks Without Activation Functions (Revised Edition)

Saburo Tenda

Announcement: Revised Edition of the Technical Note Published on Zenodo A revised edition of the technical note “A Construction Method for Neural Networks Without Activation Functions” has been published on Zenodo. This updated version includes a newly added Appendix, which provi…

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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)2026-07-24

Project ASTRA: Real-Time Activation Subspace Deflection for Deep Neural Networks via Float64 Nullspace Projection

MD Mahfooz, Alsaad Alam

Deep Neural Networks (DNNs) exhibit acute vulnerabilities to intermediate activation layer perturbations engineered through out-of-distribution (OOD) noise injection and feature-steering gradient updates. Conventional defensive paradigms—such as adversarial retraining or external…

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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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