Two configurable quantum algorithms (DG-CSWAP and DG-DST) for histopathologic cancer detection are shown to be algebraically equivalent, executed on real NISQ hardware with mitigation, and achieve 79.8% accuracy versus a classical baseline.
Pdd-agent: Multimodal large language model-driven ai agent for enhanced plant disease diagnosis
5 Pith papers cite this work. Polarity classification is still indexing.
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A new 839K-image plant disease dataset paired with an agentic visual reasoning system that uses source-grounded symptoms raises diagnosis accuracy by 16.2 points on average and generalizes to unseen crops without retraining.
K-STEMIT reduces RMSE by 21% for subsurface stratigraphy thickness estimation from radar data via a knowledge-informed spatio-temporal GNN with adaptive feature fusion and physical priors from the MAR weather model.
Dynamic Focal Attention learns class-specific difficulty via per-class biases in attention logits, improving Dice and IoU on imbalanced histopathology segmentation benchmarks.
Deep unrolling SR model with outlier removal for LiDAR point clouds shows improved pose estimation accuracy and efficiency in SLAM compared to prior SR methods.
citing papers explorer
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Configurable Algorithms for Histopathologic Cancer Detection on Quantum Hardware
Two configurable quantum algorithms (DG-CSWAP and DG-DST) for histopathologic cancer detection are shown to be algebraically equivalent, executed on real NISQ hardware with mitigation, and achieve 79.8% accuracy versus a classical baseline.
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Learning Class Difficulty in Imbalanced Histopathology Segmentation via Dynamic Focal Attention
Dynamic Focal Attention learns class-specific difficulty via per-class biases in attention logits, improving Dice and IoU on imbalanced histopathology segmentation benchmarks.