Tootfinder

Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection
Adri\'an Mar{\i}n, Mauricio Soto-Gomez, Giorgio Valentini, Elena Casiraghi, Carlos Cano, Daniel Manzano
https://arxiv.org/abs/2510.01918

Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection
Graph Representation Learning (GRL) has emerged as a cornerstone technique for analysing complex, networked data across diverse domains, including biological systems, social networks, and data analysis. Traditional GRL methods often struggle to capture intricate relationships within complex graphs, particularly those exhibiting non-trivial structural properties such as power-law distributions or hierarchical structures. This paper introduces a novel quantum-inspired algorithm for GRL, utilizing…

DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool Controllers
Navid Aftabi, Abhishek Hanchate, Satish Bukkapatnam, Dan Li
https://arxiv.org/abs/2508.21797

DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool Controllers
Industry 4.0's highly networked Machine Tool Controllers (MTCs) are prime targets for replay attacks that use outdated sensor data to manipulate actuators. Dynamic watermarking can reveal such tampering, but current schemes assume linear-Gaussian dynamics and use constant watermark statistics, making them vulnerable to the time-varying, partly proprietary behavior of MTCs. We close this gap with DynaMark, a reinforcement learning framework that models dynamic watermarking as a Markov decision p…

Synesthesia of Machines (SoM)-Based Task-Driven MIMO System for Image Transmission
Sijiang Li, Rongqing Zhang, Xiang Cheng, Jian Tang
https://arxiv.org/abs/2509.02031 https://…

Synesthesia of Machines (SoM)-Based Task-Driven MIMO System for Image Transmission
To support cooperative perception (CP) of networked mobile agents in dynamic scenarios, the efficient and robust transmission of sensory data is a critical challenge. Deep learning-based joint source-channel coding (JSCC) has demonstrated promising results for image transmission under adverse channel conditions, outperforming traditional rule-based codecs. While recent works have explored to combine JSCC with the widely adopted multiple-input multiple-output (MIMO) technology, these approaches …

Bayesian Ego-graph inference for Networked Multi-Agent Reinforcement Learning
Wei Duan, Jie Lu, Junyu Xuan
https://arxiv.org/abs/2509.16606 https://arxiv.o…

Bayesian Ego-graph inference for Networked Multi-Agent Reinforcement Learning
In networked multi-agent reinforcement learning (Networked-MARL), decentralized agents must act under local observability and constrained communication over fixed physical graphs. Existing methods often assume static neighborhoods, limiting adaptability to dynamic or heterogeneous environments. While centralized frameworks can learn dynamic graphs, their reliance on global state access and centralized infrastructure is impractical in real-world decentralized systems. We propose a stochastic gra…

Communication-aware Wide-Area Damping Control using Risk-Constrained Reinforcement Learning
Kyung-bin Kwon, Lintao Ye, Vijay Gupta, Hao Zhu
https://arxiv.org/abs/2509.23620 http…

Communication-aware Wide-Area Damping Control using Risk-Constrained Reinforcement Learning
Non-ideal communication links, especially delays, critically affect fast networked controls in power systems, such as the wide-area damping control (WADC). Traditionally, a delay estimation and compensation approach is adopted to address this cyber-physical coupling, but it demands very high accuracy for the fast WADC and cannot handle other cyber concerns like link failures or {cyber perturbations}. Hence, we propose a new risk-constrained framework that can target the communication delays, ye…

REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitations
Tianlong Yu, Lihong Liu, Ziyi Zhou, Fudu Xing, Kailong Wang, Yang Yang
https://arxiv.org/abs/2508.10701

REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitations
The exploitation of 1 day or n day vulnerabilities poses severe threats to networked devices due to massive deployment scales and delayed patching (average Mean Time To Patch exceeds 60 days). Existing defenses, including host based patching and network based filtering, are inadequate due to limited scalability across diverse devices, compatibility issues especially with embedded or legacy systems, and error prone deployment process (manual patch validation). To address these issues, we introdu…

Modeling revolutions in networked societies: learning from the Tunisian spring
Daniel Aguilar-Vel\'azquez, Denis Boyer, Robert Boyer
https://arxiv.org/abs/2508.06684 https:/…

Modeling revolutions in networked societies: learning from the Tunisian spring
Economic competition and deregulation have led to a polarization of societies between a small, increasingly powerful elite and a majority of socially excluded individuals, marginalized and unconnected to political representations. This is the breeding ground for protest movements, relayed by local ties and amplified by social networks. Based on the characteristics revealed by socio-economic research into the Arab revolutions of the 2010s, this article proposes a formalization inspired by the th…

Anomaly Detection in Networked Bandits
Xiaotong Cheng, Setareh Maghsudi
https://arxiv.org/abs/2508.20076 https://arxiv.org/pdf/2508.20076

Anomaly Detection in Networked Bandits
The nodes' interconnections on a social network often reflect their dependencies and information-sharing behaviors. Nevertheless, abnormal nodes, which significantly deviate from most of the network concerning patterns or behaviors, can lead to grave consequences. Therefore, it is imperative to design efficient online learning algorithms that robustly learn users' preferences while simultaneously detecting anomalies. We introduce a novel bandit algorithm to address this problem. Through netwo…

Power-Constrained Policy Gradient Methods for LQR
Ashwin Verma, Aritra Mitra, Lintao Ye, Vijay Gupta
https://arxiv.org/abs/2507.15806 https://

Power-Constrained Policy Gradient Methods for LQR
Consider a discrete-time Linear Quadratic Regulator (LQR) problem solved using policy gradient descent when the system matrices are unknown. The gradient is transmitted across a noisy channel over a finite time horizon using analog communication by a transmitter with an average power constraint. This is a simple setup at the intersection of reinforcement learning and networked control systems. We first consider a communication-constrained optimization framework, where gradient descent is applie…

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
Berire Gunes Reyhan, Sinem Coleri
https://arxiv.org/abs/2507.08653

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
In Wireless Networked Control Systems (WNCSs), control and communication systems must be co-designed due to their strong interdependence. This paper presents a novel optimization theory-based safe deep reinforcement learning (DRL) framework for ultra-reliable WNCSs, ensuring constraint satisfaction while optimizing performance, for the first time in the literature. The approach minimizes power consumption under key constraints, including Peak Age of Information (PAoI) violation probability, tra…

Teachers of bachelors lab courses collaborating to promote open inquiry: a case study
Lesley G. A. de Putter, Marloes M. H. G. Hendrickx
https://arxiv.org/abs/2508.10379 https:/…

Teachers of bachelors lab courses collaborating to promote open inquiry: a case study
A group of university bachelors teachers of open inquiry lab courses in science subjects collaborated in a Networked Faculty Learning Community (NFLC) with the final goal to share their course materials publicly. In this paper we describe their collaborative process, communication on the topic and their road to a common definition of open inquiry as a didactical method. The study emerged organically during the collaboration of this NFLC as the group increasingly recognized the value of document…