The paper introduces a physics‑guided deep metric learning approach for open‑world radar pulse de‑interleaving, leveraging continuous Time‑of‑Arrival sinusoidal positional encodings to model physical inter‑pulse durations. It builds on a transformer‑based framework, optimizing network parameters solely with Supervised Contrastive learning and employing physics‑based priors—PRI consistency and AoA continuity—for validation and checkpoint selection via unsupervised HDBSCAN clustering. This method aims to improve de‑interleaving performance in dense, contested electromagnetic environments where classical techniques falter.
arXiv:2608. 04706v1 Announce Type: new Abstract: Monitoring of offshore wind energy infrastructure life cycles, especially during the deployment phase, is an important contribution for stakeholders to make informed decisions in a phase of increasing deployment activities.
arXiv:2606. 13302v1 Announce Type: new Abstract: Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience.
arXiv:2607. 02537v1 Announce Type: cross Abstract: Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity.
IRENE is a deep learning model that provides probabilistic short‑range precipitation nowcasts over Italy at 1 km spatial and 5‑minute temporal resolution. It uses an encoder–forecaster architecture built on multi‑scale Convolutional Gated Recurrent Units (ConvGRUs) and is trained on national radar composites, with an importance‑sampling scheme and the almost‑fair Continuous Ranked Probability Score as its primary loss. Three training variants—standard, adversarial (IRENE‑GAN), and spectrally constrained (IRENE‑GAN‑RAPSD)—outperform benchmark methods STEPS and DGMR in probabilistic skill, though the advantage in mean absolute error is limited to the first 90 minutes.
arXiv:2607. 19787v1 Announce Type: cross Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover semantics are closely coupled with electromagnetic scattering mechanisms.
arXiv:2606. 19888v1 Announce Type: cross Abstract: Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichannel signal complexity, inherent noise, and limited labeled data.
AF-Mamba is a deep learning model that predicts atrial fibrillation (AF) onset one hour in advance using long‑term RR intervals. It combines temporal convolutional networks for local feature extraction with Mamba, a state‑space model for long‑range sequence modeling, achieving high sensitivity (0.889) and specificity (0.943) in subject‑wise testing. The model maintains strong performance across unseen datasets, offering a favorable trade‑off between predictive accuracy and computational efficiency for real‑time ambulatory monitoring.
Distinguishing periodic from chaotic dynamics in a time series is a fundamental challenge in both physics and engineering. Yet, end-to-end learned architectures must discover both a representation and...
Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover semantics are closely coupled with electromagnetic scattering mechanisms. Many existing complex-valued networks can preserve amplitude-phase information, but they are often limited in long-range spatial dependency modeling and usually incorporate polarimetric priors only as input-level or shallow auxiliary features.
arXiv:2609.39792v2 Announce Type: new Abstract: Distinguishing periodic from chaotic dynamics in a time series is a fundamental challenge in both physics and engineering. Yet, end-to-end learned arch...
arXiv:2609.22139v1 Announce Type: cross Abstract: Automatic modulation classification (AMC) of received radio signals is prudent for further signal processing tasks such as communication monitoring,...
arXiv:2607. 08031v1 Announce Type: cross Abstract: The dynamics of communication environments induce significant distribution shifts across domains, challenging the generalization of deep learning-based automatic modulation classification (AMC) models.