Optimal FALQON optimizes per-layer δ_k and M_k via classical methods, yielding statistically significant gains in success probability and efficiency over standard FALQON on 94 non-isomorphic 3-regular graphs with 12 vertices.
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FetalSleepNet achieves 86.6% accuracy in fetal sleep stage classification from ovine EEG by fine-tuning an adult model with spectral equalisation domain adaptation, outperforming baselines as the first such deep learning system.
A three-stage pipeline detects 16 landmarks, coarsely segments 12 labels, and refines them into 26 structures using landmark constraints to improve accuracy in subcortical MRI segmentation.
Physically bounded extrapolation models for zero-noise extrapolation reduce unphysical predictions and improve stability compared to unbounded fits on large synthetic benchmarks and real hardware.
PCA-Triage adaptively sets sensor sampling rates from incremental PCA loadings to meet bandwidth limits while preserving downstream inference F1 scores close to full-data performance.
Curvature-based importance density functions enable dynamic grid adaptation in KANs, cutting relative errors by 25.3% on synthetic functions, 9.4% on Feynman data, and 23.3% on Helmholtz PDEs versus input-density baselines.
SPARC articulatory features predict sEMG signals more accurately than phoneme features across aloud, mimed, and subvocal speech, with consistent anatomical patterns and above-chance performance even in silent mode.
Combining LLM-based elderly-contextual paraphrasing with TTS synthesis using elderly speakers reduces word error rates in elderly ASR by up to 58% over standard Whisper baselines on English and Korean datasets.
Ensemble of three binary DNNs classifies network flows as benign, DoS or DDoS at 99.84% and 95.30% accuracy on CICIDS2018 and UNSW-NB15, paired with RAG to generate mitigation reports that outperform vanilla LLM outputs.
ADAPT uses an EWMA estimator for cold-start durations to set a dynamic horizon in an MPC-based proactive autoscaler, achieving under 5% SLA violations with MPC+LSTM across tested workloads versus higher rates for HPA and MPC+Prophet.
SODA-CitrON performs online clustering of heterogeneous sensor detections to associate and track static objects, outperforming JPDA, DBSTREAM, and POM-based methods on F1 score, position RMSE, MOTP, and MOTA in Monte Carlo simulations.
citing papers explorer
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Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning
Optimal FALQON optimizes per-layer δ_k and M_k via classical methods, yielding statistically significant gains in success probability and efficiency over standard FALQON on 94 non-isomorphic 3-regular graphs with 12 vertices.
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FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep Stage Classification
FetalSleepNet achieves 86.6% accuracy in fetal sleep stage classification from ovine EEG by fine-tuning an adult model with spectral equalisation domain adaptation, outperforming baselines as the first such deep learning system.
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Automatic Landmark-Based Segmentation of Human Subcortical Structures in MRI
A three-stage pipeline detects 16 landmarks, coarsely segments 12 labels, and refines them into 26 structures using landmark constraints to improve accuracy in subcortical MRI segmentation.
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Improving Zero-Noise Extrapolation via Physically Bounded Models
Physically bounded extrapolation models for zero-noise extrapolation reduce unphysical predictions and improve stability compared to unbounded fits on large synthetic benchmarks and real hardware.
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PCA-Driven Adaptive Sensor Triage for Edge AI Inference
PCA-Triage adaptively sets sensor sampling rates from incremental PCA loadings to meet bandwidth limits while preserving downstream inference F1 scores close to full-data performance.
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A Dynamic Framework for Grid Adaptation in Kolmogorov-Arnold Networks
Curvature-based importance density functions enable dynamic grid adaptation in KANs, cutting relative errors by 25.3% on synthetic functions, 9.4% on Feynman data, and 23.3% on Helmholtz PDEs versus input-density baselines.
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Comparison of sEMG Encoding Accuracy Across Speech Modes Using Articulatory and Phoneme Features
SPARC articulatory features predict sEMG signals more accurately than phoneme features across aloud, mimed, and subvocal speech, with consistent anatomical patterns and above-chance performance even in silent mode.
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Elderly-Contextual Data Augmentation via Speech Synthesis for Elderly ASR
Combining LLM-based elderly-contextual paraphrasing with TTS synthesis using elderly speakers reduces word error rates in elderly ASR by up to 58% over standard Whisper baselines on English and Korean datasets.
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From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation
Ensemble of three binary DNNs classifies network flows as benign, DoS or DDoS at 99.84% and 95.30% accuracy on CICIDS2018 and UNSW-NB15, paired with RAG to generate mitigation reports that outperform vanilla LLM outputs.
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ADAPT: A Self-Calibrating Proactive Autoscaler for Container Orchestration
ADAPT uses an EWMA estimator for cold-start durations to set a dynamic horizon in an MPC-based proactive autoscaler, achieving under 5% SLA violations with MPC+LSTM across tested workloads versus higher rates for HPA and MPC+Prophet.
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SODA-CitrON: Static Object Data Association by Clustering Multi-Modal Sensor Detections Online
SODA-CitrON performs online clustering of heterogeneous sensor detections to associate and track static objects, outperforming JPDA, DBSTREAM, and POM-based methods on F1 score, position RMSE, MOTP, and MOTA in Monte Carlo simulations.