Influpaint uses generative diffusion models on image-encoded influenza data to produce realistic and diverse epidemic trajectories that match leading ensemble methods in accuracy.
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Releases the DAPWH dataset of 3556 wasp images including 1739 COCO-annotated examples to enable AI models for identifying Ichneumonoidea and associated families.
SynthRAD2025 shows deep learning produces synthetic CTs with MAE 48-65 HU and high dosimetric gamma passing rates for radiotherapy, performing better on CBCT-to-CT than MRI-to-CT tasks.
Exploiting linear structure in VLM embeddings, a synthetic-data pre-training method yields background-invariant representations that exceed 90% worst-group accuracy on Waterbirds even under 100% spurious correlation with no minority examples in training.
AIMIP Phase 1 sets up a common experiment and five evaluation criteria for AI atmosphere models forced by historical sea surface temperatures, finding they match conventional models on most metrics but underestimate some warming trends and diverge on out-of-sample tests.
A graph-regulated disentangling Mamba model with sparse tokens achieves 93.94% accuracy classifying tree species from MODIS time series in Alberta and outperforms twelve prior models.
Alignment pattern analysis reveals that models aligned to individual brain ROIs do not reproduce the stable cross-region alignment profiles observed across human subjects.
PhysioLite delivers Transformer-comparable ECG/EMG performance using learnable wavelet filters and hardware-aware design at ~370KB quantized size on μNPUs.
A physics-informed CNN predicts pore-scale velocity fields from geometry and serves as a warm-start to accelerate Lattice-Boltzmann solvers in over 90% of tested cases.
FedSSG generates and shares synthetic samples within a federated setup to reduce class imbalance and domain shift problems in medical image classification.
HLGFA detects anomalies by identifying breakdowns in cross-resolution feature consistency between high- and low-resolution views of normal samples, guided by structure and detail priors, and reports 97.9% pixel AUROC on MVTec AD.
InternVL scales a vision model to 6B parameters and aligns it with LLMs using web data to achieve state-of-the-art results on 32 visual-linguistic benchmarks.
The NTIRE 2026 challenge reports strong performance from 17 teams on raindrop removal for dual-focused day and night images using an adjusted real-world dataset with 14,139 training images.
citing papers explorer
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Generative diffusion models for spatiotemporal influenza forecasting
Influpaint uses generative diffusion models on image-encoded influenza data to produce realistic and diverse epidemic trajectories that match leading ensemble methods in accuracy.
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Descriptor: Parasitoid Wasps and Associated Hymenoptera Dataset (DAPWH)
Releases the DAPWH dataset of 3556 wasp images including 1739 COCO-annotated examples to enable AI models for identifying Ichneumonoidea and associated families.
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Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report
SynthRAD2025 shows deep learning produces synthetic CTs with MAE 48-65 HU and high dosimetric gamma passing rates for radiotherapy, performing better on CBCT-to-CT than MRI-to-CT tasks.
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Birds of a Feather Flock Together: Background-Invariant Representations via Linear Structure in VLMs
Exploiting linear structure in VLM embeddings, a synthetic-data pre-training method yields background-invariant representations that exceed 90% worst-group accuracy on Waterbirds even under 100% spurious correlation with no minority examples in training.
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AIMIP Phase 1: systematic evaluations of AI weather and climate models
AIMIP Phase 1 sets up a common experiment and five evaluation criteria for AI atmosphere models forced by historical sea surface temperatures, finding they match conventional models on most metrics but underestimate some warming trends and diverge on out-of-sample tests.
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A Novel Graph-Regulated Disentangling Mamba Model with Sparse Tokens for Enhanced Tree Species Classification from MODIS Time Series
A graph-regulated disentangling Mamba model with sparse tokens achieves 93.94% accuracy classifying tree species from MODIS time series in Alberta and outperforms twelve prior models.
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Only Brains Align with Brains: Cross-Region Alignment Patterns Expose Limits of Normative Models
Alignment pattern analysis reveals that models aligned to individual brain ROIs do not reproduce the stable cross-region alignment profiles observed across human subjects.
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Towards Real-Time ECG and EMG Modeling on $\mu$NPUs
PhysioLite delivers Transformer-comparable ECG/EMG performance using learnable wavelet filters and hardware-aware design at ~370KB quantized size on μNPUs.
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Physics-informed convolutional neural networks for fluid flow through porous media
A physics-informed CNN predicts pore-scale velocity fields from geometry and serves as a warm-start to accelerate Lattice-Boltzmann solvers in over 90% of tested cases.
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Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generation
FedSSG generates and shares synthetic samples within a federated setup to reduce class imbalance and domain shift problems in medical image classification.
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HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection
HLGFA detects anomalies by identifying breakdowns in cross-resolution feature consistency between high- and low-resolution views of normal samples, guided by structure and detail priors, and reports 97.9% pixel AUROC on MVTec AD.
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InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
InternVL scales a vision model to 6B parameters and aligns it with LLMs using web data to achieve state-of-the-art results on 32 visual-linguistic benchmarks.
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NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
The NTIRE 2026 challenge reports strong performance from 17 teams on raindrop removal for dual-focused day and night images using an adjusted real-world dataset with 14,139 training images.