In order to provide offloading services for computationally demanding and time-sensitive operations on vehicles, multi-access edge computing has emerged as a successful paradigm. However, there are typically spatiotemporal load mismatches across edge servers due to significant vehicle mobility. As a result, task migrat...
Shi Dong, Suliman A. Alsuhibany, A. Smerat et al.· Discover Computing· 0 citations
The El-Rakhawi Modular Autonomous Biomanufacturing System (MABS) (DOI: 10.5281/zenodo.23305330) presents a groundbreaking compact platform for decentralized therapeutic production, integrating four core subsystems within a footprint under 4 square meters: (1) Modular Microfluidic Cell Processing Unit (MMCPU) with dispo...
mohamed kamal arafa el-rakhawi· Zenodo (CERN European Organi...· 0 citations
The paper investigates how the choice of a generative stochastic model of the training environment affects the numerical stability of the learning and the transfer of the Double DQN policy to real time series. Four environments — GBM, GARCH(1,1)-t, Minimal Neural SDE, and Neural Jump-Diffusion — are compared, created o...
Yurchenko Yuriy, Olexandr Zakovorotnyi· Journal of Mathematical Mode...· 0 citations
Code release – v1.0.0 This release contains the source code accompanying the manuscript "Adversarial Observations in State-Space Models for Robust Reinforcement Learning" by M. Santos-Pascual and D. Ríos Insua. The repository provides implementations and experimental scripts for studying adversarial perturbations of ob...
Miguel Santos Pascual· Zenodo (CERN European Organi...· 0 citations
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AI-Driven Cyber Resilience Framework for Critical Digital Infrastructure Using Reinforcement Learning Modern digital infrastructure faces increasingly complex cybersecurity threats. From healthcare networks and banking systems to power grids, telecommunications, cloud platforms, and industrial control systems, protecti...
Dr. T. Samraj Lawrence, Rupal Snehkunj, S. Ramya· Zenodo (CERN European Organi...· 0 citations
Source code, configuration, result files, figure and table masters, and the frozen policy checkpoints for the article of the same title. Reproduction: python harness9/tables.py and python harness9/figures.py rebuild every table and figure in the article from the stored result files; the simulator is not re-run. This wo...
Miroslav B. Milovanovic, Jianxun Cui, Dragan Antić et al.· Zenodo (CERN European Organi...· 0 citations
In dynamic and complex environments, visual reinforcement learning (Visual RL) agents struggle to extract task-relevant information, achieve stable policy optimisation, and generalise across changing conditions. This paper proposes DA-RL, an attention-enhanced and decoupled reinforcement learning framework for cyber-ph...
Reliable and energy-efficient data collection remains a fundamental challenge in Underwater Acoustic Sensor Networks (UASNs) owing to limited battery capacity, high propagation delay, dynamic network topology and unreliable communication links. To address these challenges, we propose a Cooperative Multi-Agent Reinf...
Adi Surendra Mohanraju M, A. Lokam, Kiran Dasari· Scientific Reports· 0 citations
Animals continuously transform sensory inputs into representations that guide behavior. Although the amygdala is a brain structure critical for learning and choice, how natural sensory experience is represented within this circuit remains unknown. Here, we identify a representation of the visual environment in the huma...
Grace Jang, Kierstin M. Riels, Elias Aberman et al.· bioRxiv (Cold Spring Harbor...· 0 citations
Simulator, controllers, scenarios, trained model and per-episode results for the 33-bus feeder study comparing rule-based, reinforcement-learning and agentic control under faults, storms and telemetry-spoofing cyberattacks. Python 3; see README.md.
Angel Sayani· Zenodo (CERN European Organi...· 0 citations
Abstract rule learning and cognitive flexibility are central to adaptive behavior because they allow organisms to identify regularities, generalize knowledge to unfamiliar situations, and modify behavioral strategies when environmental contingencies change. Although these capacities have traditionally been investigated...
Reinforcement learning (RL)-based adversarial scenario generation through dynamic agent–environment interactions has emerged as a promising approach for accelerating autonomous vehicle (AV) testing. However, existing RL approaches struggle to balance adversariality and naturalness because naturalistic driving behaviors...
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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