488 papers
This paper introduces C3Q-MARL, a connectivity-aware framework that integrates graph-theoretic topology control with a QAOA-inspired optimization module into multi-agent reinforcement learning to enhance UAV swarm coordination, collision avoidance, and connectivity preservation under partial observability and communication constraints.
This study presents the first scale-by-scale benchmark of YOLOv11 and YOLOv12 variants on the BCCD dataset, revealing that while their overall mean performance is nearly identical, YOLOv12 excels at the nano scale for latency-critical applications whereas YOLOv11 outperforms it at medium and large scales, indicating that model selection should be driven by specific scale and recall requirements rather than a uniform assumption of superiority.
This paper introduces RLHOARNet, a MONAI-based 3D U-Net architecture featuring a lightweight residual boundary refinement module that achieves state-of-the-art performance and real-time inference speeds for multi-modal brain tumor segmentation on the BraTS 2020 dataset.
FedHyperM is a privacy-preserving federated learning framework for edge devices that addresses modality heterogeneity by employing hypergraph-based prediction and modality-sensitive aggregation to significantly outperform existing baselines in accuracy and F1-score.
This paper proposes the APT-VFSEA model, which utilizes variable fuzzy sets to quantitatively evaluate the effectiveness of Advanced Persistent Threat (APT) attacks and defenses by addressing their inherent uncertainty and complexity, with experimental validation demonstrating its accuracy against real-world scenarios.
This study proposes an adaptive selection method for MICE algorithm parameters based on pulmonary function indicators, demonstrating that this strategy outperforms PCHIP interpolation by maintaining stable predictive performance and effectively capturing multivariate correlations in COPD datasets with varying missing rates.
This paper introduces the Oversight-to-Control (O2C) framework, a socio-technical model comprising six functions and supporting conditions, to diagnose why human oversight often fails to achieve effective control in high-risk AI systems by analyzing regulatory instruments and real-world cases where interpretation and timely intervention frequently break down.
This paper proposes AJIT, a high-fidelity classical–quantum hybrid framework that ensures quantum-resilient secure sharing and multiparty authentication of DICOM medical images by combining DTCWT-based compression, quasi-periodic unitary scrambling, and topological hypergraph verification to achieve lossless reconstruction and robust differential resistance.
This study introduces the Cross-Market Retrieval-Augmented Lag-Llama (CM-RAF-Lag-Llama) framework, demonstrating that integrating historical retrieval with a pre-trained Lag-Llama model significantly reduces forecasting errors across diverse equity markets by leveraging analogous historical windows to correct predictions, particularly for volatility and volume-related targets.
The paper proposes CFE-UNet, a novel semantic segmentation framework for remote sensing that integrates a reconstructed encoder with a Cross-Feature Exchange decoder to effectively combine global context and local details, achieving state-of-the-art performance on complex, heterogeneous urban and natural scenes.