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PVminerLLM2 Uses Preference Optimization to Improve Structured Patient Voice Extraction Beyond Models: Reflections on Engineering AI-enabled Systems in a Project-Based Course AutoDojo: Adaptive Attacks Expose Superficial Defenses and Structural Limits in LLM Agents Calibrated Variance Propagation Cuts Uncertainty Estimation Cost for Deep Learning Models Patel Engineering Joint Venture Secures ₹126 Crore Tasgaon Lift Irrigation Project in Maharashtra EgoPhys Framework Creates Deformable Object Digital Twins from Single Egocentric Video Fast When, Careful Who: Dual-Process Multiparty Turn-Taking with Diffusion Augmentation Ensemble Deep Learning Achieves 99.27% Accuracy in Lemon Leaf Disease Detection India's structural growth story remains strong despite global uncertainties: Ventura Lossy Compression Slashes Storage 39x for Neural Surrogate Models, Study Finds PVminerLLM2 Uses Preference Optimization to Improve Structured Patient Voice Extraction Beyond Models: Reflections on Engineering AI-enabled Systems in a Project-Based Course AutoDojo: Adaptive Attacks Expose Superficial Defenses and Structural Limits in LLM Agents Calibrated Variance Propagation Cuts Uncertainty Estimation Cost for Deep Learning Models Patel Engineering Joint Venture Secures ₹126 Crore Tasgaon Lift Irrigation Project in Maharashtra EgoPhys Framework Creates Deformable Object Digital Twins from Single Egocentric Video Fast When, Careful Who: Dual-Process Multiparty Turn-Taking with Diffusion Augmentation Ensemble Deep Learning Achieves 99.27% Accuracy in Lemon Leaf Disease Detection India's structural growth story remains strong despite global uncertainties: Ventura Lossy Compression Slashes Storage 39x for Neural Surrogate Models, Study Finds

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brain-computer interface

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EEGNet Study Reveals Key Limitations in fNIRS Cognitive Load Classification

Artificial Intelligence #eegnet#fnirs

EEGNet Study Reveals Key Limitations in fNIRS Cognitive Load Classification

A comprehensive study published on arXiv systematically evaluates EEGNet for classifying cognitive load from fNIRS signals. The research highlights critical challenges in generalization, achieving only 56.11% accuracy under subject-independent evaluation, and underscores the importance of segmentation strategy and learning rate selection.

Jun 16, 2026 1 source

Subject-Specific Encoders Improve Cross-Subject EEG Decoding, Study Finds

Artificial Intelligence #eeg#brain-computer interface

Subject-Specific Encoders Improve Cross-Subject EEG Decoding, Study Finds

A new study on arXiv.org proposes replacing shared EEG encoders with subject-specific encoders to handle inter-subject distribution shifts. The hybrid model, tested on four motor-imagery datasets, internalises Euclidean Alignment and increases class distinctiveness, though head selection for unseen subjects remains a bottleneck.

Jun 16, 2026 1 source