This paper presents a framework that integrates input-to-state string stability (ISSS) theory with reinforcement learning to achieve stable, scalable, and adaptive swarm coordination. First, a virtual string perception decomposes $N$-agent interactions into parallel robot-following subtasks by synthesizing local virtua...
Yu-Chen Wei, Wenke E, Yu-Hsiang Su et al.· 2026 IEEE 22nd International...· 0 citations
Neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and multiple sclerosis, encompass a wide range of chronic conditions with irreversible damage to the central nervous system. Current diagnostic workups of these disorders rely on invasive, time-consuming, and costly tests, such as magnetic...
Ali Aghababaei, Masoud Etemadifar, Amir Atapour-Abarghouei et al.· Annual Review of Vision Scie...· 0 citations
A Hyper Spatial-Temporal Graph (HSTG), which decomposes global spatial-temporal reasoning into local spatial-temporal receptive fields around individual joint-time nodes by extending per-frame skeleton graphs into temporal neighbourhoods, thereby enabling structure-aware coupled reasoning while preserving local structu...
Ruochen Li, Shuang Chen, E. Wenke et al.· 0 citations
Large language models (LLMs) are increasingly used for model-to-code generation, but they weaken one of the central properties of model-driven engineering: traceability between design models and generated implementation artefacts. Existing approaches either rely on deterministic transformations, where trace links can b...
Marc North, Nelly Bencomo, Amir Atapour-Abarghouei· Proceedings of the ACM/IEEE...· 0 citations
Sarse Multi-Stage Expert-Agent Routing is proposed, a language-based clinical reasoning framework that models diagnosis as a stage-wise routing process and adaptively activates a sparse set of medical expert agents, supported by expert-specific memory across stages.
Sike Xiang, Shuang Chen, Qianpeng Sun et al.· 1 citation
Overall, legal information processing benefits from different inductive biases across tasks, with cross-architecture ensembling, feature-based reranking and retrieval-augmented prompting each proving most effective in different settings.
Amal Saad Alshehri, N. Bencomo, Amir Atapour-Abarghouei· 0 citations
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