Author

Usa

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Preprint Aug 2026

Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT

Background Lung cancer remains the deadliest cancer worldwide because it is often diagnosed too late. Effective treatment depends on detection at an early screening stage. However, the growing number of patients and the limited number of radiologists lead to prolonged diagnostic waiting times. In very early stage lung...

A. Mrukwa, M. Socha, A. Suwalska et al. · 0 citations

Preprint Aug 2026

Drone-Assisted UAV-UGV Collaboration for Autonomous Navigation in Snow-Covered Terrain

A custom efficient U-Net architecture is introduced that falls under the computational constraints for real-time road segmentation, utilizing a novel synthetic snow data augmentation technique to achieve 96.5% segmentation accuracy.

Shreya Gupta, P. Agrawal, Priya Gupta et al. · 0 citations

Jul 2026

From Distances to Trajectories: Real-Time Signed Distance Function Mapping and Distance-Accelerated Motion Planning for UAVs

An Octree REsidual Network (OREN) is developed that pairs an explicit octree prior with an implicit neural residual to reconstruct SDFs online from point cloud observations with the efficiency of volumetric methods and the accuracy and differentiability of neural methods.

Jason Stanley, Zhi-Rui Dai, Qi-Hao Qian et al. · arXiv.org · 0 citations

#machine learning Preprint Apr 2026

AutoREC: A reinforcement learning platform for equivalent circuit model generation

The platform supports an end-to-end workflow encompassing EIS preprocessing with selectable impedance representations, agent setup and training, ECM generation for new measurements, and visualization-based evaluation and analysis of agent decision-making.

A. Jaberi, Yonatan Kurniawan, Robert Black et al. · 0 citations

#small language model Preprint Aug 2026

uMOF: A Universal Database, Benchmark, and Machine Learning Interatomic Potentials for Metal-Organic Frameworks

Foundation machine learning interatomic potentials (MLIPs) deliver near-ab-initio accuracy at a fraction of the computational cost, yet their promise for Metal-organic Frameworks (MOFs) remains largely unrealized as large unit cells make first-principles training data expensive to generate, fine-tuned models are scarce...

T. J. Inizan, Prathami Divakar Kamath, Alin M. Elena et al. · 0 citations

Preprint Aug 2026

Physics-Informed Stochastic Configuration Machine: A Backpropagation-Free Neural Network with Fast Training for Nonlinear Differential Equations

The Physics-Informed Stochastic Configuration Machine is proposed, a novel backpropagation-free framework for both forward and inverse problems in differential equations that achieves high-fidelity predictive accuracy and robust parameter identification while accelerating the training process by orders of magnitude com...

Yueze Song, Zhong-Zhe Chen, Li-Hui Cen et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring

The results support neuromorphic acoustic anomaly detection as a practical candidate for low-power, persistent machine monitoring and support autoencoder-based acoustic anomaly detection on an Intel Loihi 2 neuromorphic processor.

Steven C. Nesbit, Victor M. Vergara, Michael A. Felix et al. · 0 citations