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Latent-Feature-Informed Neural ODE Modeling for Lightweight Stability Evaluation of Black-box Grid-Tied Inverters
Jialin Zheng, Zhong Liu, Xiaonan Lu
https://arxiv.org/abs/2510.09826

Latent-Feature-Informed Neural ODE Modeling for Lightweight Stability Evaluation of Black-box Grid-Tied Inverters
Stability evaluation of black-box grid-tied inverters is vital for grid reliability, yet identification techniques are both data-hungry and blocked by proprietary internals. {To solve this, this letter proposes a latent-feature-informed neural ordinary differential equation (LFI-NODE) modeling method that can achieve lightweight stability evaluation directly from trajectory data.} LFI-NODE parameterizes the entire system ODE with a single continuous-time neural network, allowing each new sample…

Triangular neural synchronization patterns in visual impairment: A comprehensive case series exploring multi-node network dynamics and the Neural Triangle Index (NTI) https://www.

Triangular Neural Synchronization Patterns in Visual Impairment: A Comprehensive Case Series Exploring Multi-node Network Dynamics and the Neural Triangle Index
Background: Compensatory neural plasticity in visual impairment involves complex reorganization of multi-node networks that goes beyond conventional bilateral connection analysis. Triangular synchronization patterns, representing coordinated activity among three cortical sites simultaneously, remain unexplored despite their potential to reveal fundamental mechanisms underlying compensatory neuroplasticity. Objective: This study aimed to conduct a case series examining triangular neural synchro…

Triangular neural synchronization patterns in visual impairment https://www.cureus.com/articles/409884-triangular-neural-synchronization-patterns-i…

Triangular Neural Synchronization Patterns in Visual Impairment: A Comprehensive Case Series Exploring Multi-node Network Dynamics and the Neural Triangle Index
Background: Compensatory neural plasticity in visual impairment involves complex reorganization of multi-node networks that goes beyond conventional bilateral connection analysis. Triangular synchronization patterns, representing coordinated activity among three cortical sites simultaneously, remain unexplored despite their potential to reveal fundamental mechanisms underlying compensatory neuroplasticity. Objective: This study aimed to conduct a case series examining triangular neural synchro…

A Graph-Neural-Network-Entropy model of vital node identification on network attack and propagation
Huaizhi Liao, Tian Qiu, Guang Chen
https://arxiv.org/abs/2509.18325 https://

A Graph-Neural-Network-Entropy model of vital node identification on network attack and propagation
Vital nodes usually play a key role in complex networks. Uncovering these nodes is an important task in protecting the network, especially when the network suffers intentional attack. Many existing methods have not fully integrated the node feature, interaction and state. In this article, we propose a novel method (GNNE) based on graph neural networks and information entropy. The method employs a Graph Convolutional Network (GCN) to learn the nodes' features, which are input into a Graph Attent…

Bootstrap Learning for Combinatorial Graph Alignment with Sequential GNNs
Marc Lelarge
https://arxiv.org/abs/2510.03086 https://arxiv.org/pdf/2510.03086

Bootstrap Learning for Combinatorial Graph Alignment with Sequential GNNs
Graph neural networks (GNNs) have struggled to outperform traditional optimization methods on combinatorial problems, limiting their practical impact. We address this gap by introducing a novel chaining procedure for the graph alignment problem, a fundamental NP-hard task of finding optimal node correspondences between unlabeled graphs using only structural information. Our method trains a sequence of GNNs where each network learns to iteratively refine similarity matrices produced by previous …

Design-Space Exploration of Distributed Neural Networks in Low-Power Wearable Nodes
Meghna Roy Chowdhury, Ming-che Li, Archisman Ghosh, Md Faizul Bari, Shreyas Sen
https://arxiv.org/abs/2509.14540

Design-Space Exploration of Distributed Neural Networks in Low-Power Wearable Nodes
Wearable devices are revolutionizing personal technology, but their usability is often hindered by frequent charging due to high power consumption. This paper introduces Distributed Neural Networks (DistNN), a framework that distributes neural network computations between resource-constrained wearable nodes and resource-rich hubs to reduce energy at the node without sacrificing performance. We define a Figure of Merit (FoM) to select the optimal split point that minimizes node-side energy. A cu…

Crosslisted article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
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- A Graph-Neural-Network-Entropy model of vital node identification on network attack and propagation
Huaizhi Liao, Tian Qiu, Guang Chen