# Anomaly Detection

Deep learning models for electrocardiograms are mostly trained with supervision tied to specific diagnostic labels, which makes them fragile when patient populations or acquisition settings change. This self-supervised pretraining framework learns features from 918,915 unlabeled 12-lead ECG recordings, with one path explicitly measuring each lead's deviation from normal waveform morphology and another reconstructing cross-lead associations under those deviation cues. With the feature extractor frozen and no downstream fine-tuning, it improves AUROC by up to 6.5% and F1 by up to 12.8% over prior methods on four public datasets.

Deep learning models for electrocardiograms are mostly trained with supervision tied to specific diagnostic labels, which makes them fragile when patient populations or acquisition settings change. This self-supervised pretraining framework learns features from 918,915 unlabeled 12-lead ECG recordings, with one path explicitly measuring each lead's deviation from normal waveform morphology and another reconstructing cross-lead associations under those deviation cues. With the feature extractor frozen and no downstream fine-tuning, it improves AUROC by up to 6.5% and F1 by up to 12.8% over prior methods on four public datasets.

For runtime attack detection in cyber-physical systems, PIA has a large language model agent write physical residuals such as pump characteristics, tank mass balances and controller logic from a plant description and normal operating data, then test each candidate on an evaluation workbench before deployment. On the BATADAL water-distribution attack benchmark's test attacks, PIA with Claude Opus 5.5 reaches an F1 of 0.878 against 0.504 for the strongest of six statistical and deep baselines; ablations show that denying the agent data testing costs up to 0.28 F1.

For runtime attack detection in cyber-physical systems, PIA has a large language model agent write physical residuals such as pump characteristics, tank mass balances and controller logic from a plant description and normal operating data, then test each candidate on an evaluation workbench before deployment. On the BATADAL water-distribution attack benchmark's test attacks, PIA with Claude Opus 5.5 reaches an F1 of 0.878 against 0.504 for the strongest of six statistical and deep baselines; ablations show that denying the agent data testing costs up to 0.28 F1.

Collider analyses have trained a separate model per task; EVENET instead represents each collision as an unordered particle cloud and pretrains one backbone on 500 million simulated events with self-supervised plus physics-informed objectives, then adapts it through interchangeable task heads. In an anti-isolated ϒ→μ+μ− search on CMS Open Data, the fully pretrained variant reaches a median local significance of 7.6σ after calibration, above the published 6.4σ, and across a heavy-resonance mass grid its maximum SIC averages about 50% above training from scratch.

Collider analyses have trained a separate model per task; EVENET instead represents each collision as an unordered particle cloud and pretrains one backbone on 500 million simulated events with self-supervised plus physics-informed objectives, then adapts it through interchangeable task heads. In an anti-isolated ϒ→μ+μ− search on CMS Open Data, the fully pretrained variant reaches a median local significance of 7.6σ after calibration, above the published 6.4σ, and across a heavy-resonance mass grid its maximum SIC averages about 50% above training from scratch.

This review synthesizes current evidence on the use of artificial intelligence and machine learning in preclinical nonhuman primate xenotransplantation, noting that computer vision can support continuous noninvasive behavioral phenotyping and pain assessment, anomaly detection algorithms can extract early warning signals from biosignal streams, multiomics integration can identify xenograft-specific biomarker signatures, digital twin frameworks may enable hypothesis-generating simulations of prospective human recipient responses, and explainable AI can make model outputs more transparent and regulatory defensible, and it proposes a research agenda through which AI-augmented nonhuman primate experimentation can become a bridge from preclinical data complexity to clinical precision in xenotra

This review synthesizes current evidence on the use of artificial intelligence and machine learning in preclinical nonhuman primate xenotransplantation, noting that computer vision can support continuous noninvasive behavioral phenotyping and pain assessment, anomaly detection algorithms can extract early warning signals from biosignal streams, multiomics integration can identify xenograft-specific biomarker signatures, digital twin frameworks may enable hypothesis-generating simulations of prospective human recipient responses, and explainable AI can make model outputs more transparent and regulatory defensible, and it proposes a research agenda through which AI-augmented nonhuman primate experimentation can become a bridge from preclinical data complexity to clinical precision in xenotra