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
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6verdicts
UNVERDICTED 6representative citing papers
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.
COGENT is a continuous graph emulator using Neural ODEs for stable long-term forecasting on irregular geospatial meshes, evaluated on ice-sheet simulations with improved stability over autoregressive baselines.
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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SAGE: Scalable Agentic Grounded Evaluation for Crop Disease Diagnosis
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.
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K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data
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.
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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.
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Fast and Accurate Outlier-Aware LiDAR Super-Resolution for SLAM Applications
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.
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COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting
COGENT is a continuous graph emulator using Neural ODEs for stable long-term forecasting on irregular geospatial meshes, evaluated on ice-sheet simulations with improved stability over autoregressive baselines.