898 papers
This paper presents a deterministic, concept-grounded framework for flight simulator training that maps subsymbolic evidence to admissible scenario adaptations via a constrained machine learning policy, ensuring trustworthiness through formal safety guarantees and demonstrating empirical effectiveness in approximating a verified baseline policy.
This paper proposes a multi-modal computational framework that fuses gait video, inertial signals, plantar pressure, and knee flexion data to achieve high-accuracy intelligent assessment of knee function and exercise risk prediction for elderly patients, demonstrating superior stability and robustness compared to existing baseline models.
This study demonstrates that Submanifold Sparse Convolutional Networks (SSCN) achieve high-precision, operational-grade Airborne LiDAR Bathymetry processing with 97.2% accuracy and 99.7% coverage using minimal training data, establishing a reproducible benchmark for automated coastal surveying and nautical charting.
This study proposes a GPT-4 framework for automating key healthcare administrative documents, such as discharge summaries and insurance claims, demonstrating high fluency and coherence through prompt engineering while highlighting the need for future work to address factual accuracy and real-world integration.
This paper introduces a Monte-Carlo Compressive Optimization algorithm that leverages random queries to estimate generalized moments and a repurposed compressive sensing greedy algorithm to solve combinatorial optimization problems, offering competitive performance against dual annealing, theoretical justification, and tunable adaptability to computational resources.
This paper proposes FedSAG-IVW, a federated learning framework that combines local self-attention mechanisms with inverse variance weighting and Tikhonov regularization to effectively detect fake news while addressing data heterogeneity and preserving data privacy, achieving superior accuracy across multiple datasets.
The paper introduces DenoisedSR, a solver-agnostic front-end that learns variable-support priors to significantly accelerate and improve the accuracy of symbolic regression for discovering physical laws by filtering irrelevant variables before they reach the backend solver.
This paper introduces the Structure-Aware Collaborative Generative Framework (SACGF), a novel pipeline integrating Stable Diffusion, ControlNet, and SAM to achieve high-fidelity digital reconstruction of lacquer art surfaces by balancing structural consistency, texture fidelity, and style coherence, validated by a new quantitative metric and a specialized dataset.
This paper proposes SEM-EDGE-X, an intent-aware semantic edge intelligence framework that integrates semantic feature extraction, adaptive routing, and multi-agent coordination to significantly outperform conventional communication models in low-latency, dynamic 6G UAV–vehicular networks.
This paper demonstrates that machine learning models, particularly Recurrent Neural Networks, can accurately predict waiting times in complex, time-varying priority queueing systems using only queue length and customer priority, outperforming tree-based methods across non-preemptive and various preemptive service rules.