STREAM decouples text and music conditioning in a diffusion transformer via AdaLN for structure and BEAM for beats, plus new Motorica++ dataset and editability metrics, claiming SOTA music alignment with preserved semantics.
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GPROF-IR is a CNN-based retrieval that uses temporal context in geostationary IR observations to produce precipitation estimates with lower error than prior IR methods and climatological consistency with PMW retrievals for integration into IMERG V08.
AttentionBender applies 2D transforms to cross-attention maps in video diffusion transformers, producing distributed distortions and glitch aesthetics that reveal entangled attention mechanisms while serving as both an XAI probe and creative tool.
A LoRA-adapted conditional diffusion surrogate for electromagnetic calorimeter showers matches key observables within 2% RMSE and reproduces directional trends in design-utility gradients.
OOD-SEG reframes multi-class segmentation from sparse positive-only annotations as pixel-wise positive-unlabelled learning solved by integrating out-of-distribution detection techniques, with a proposed cross-validation evaluation on surgical imaging datasets.
Proposes a cyclic 2.5D perceptual loss with manufacturer SUVR standardization for T1w MRI to tau PET synthesis, reporting improved regional agreement on ADNI and SCAN cohorts across U-Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix.
DeepMine-Mamba adds an Anti-Dilution Gate to Mamba-based models to counteract feature dilution in document binarization and reports competitive FM and Fps scores on DIBCO benchmarks under leave-one-year-out evaluation.
High magnetic fields directly enhance the amplitude and correlation length of stripe order in a cuprate superconductor far above the vortex melting transition, indicating a coupling mechanism independent of superconductivity suppression.
CrackGeoFM is a multi-task framework that adapts a frozen visual foundation model with FCEM, CFAM, and SMTD modules for crack mask prediction, skeleton reconstruction, and uncertainty estimation, reporting SOTA results across 20 datasets including few-shot settings.
A Jacobian sensitivity curve computed at initialization identifies the narrowest U-Net configuration that avoids performance collapse, matching nnU-Net accuracy with 400-1600x fewer parameters on six medical datasets.
Commutativity regularization mitigates transient error amplification in autoregressive neural simulators by penalizing non-normality and non-commutativity of Jacobians, yielding stable long-horizon rollouts.
Spectral analysis of activations and gradients provides new diagnostics that link batch size to representation geometry, early covariance tails to token efficiency, and spectral shifts to learning dynamics in decoder-only LLMs, backed by a mechanistic model.
A recurrent Vision Transformer hypernetwork injects context into Flux Neural Operators to infer and solve unseen conservation laws while preserving robustness and long-time stability.
SIAM achieves state-of-the-art whole-head MRI segmentation of 16 structures including extra-cerebral tissues by training on synthetic data from just six manual templates, matching or exceeding prior methods on 301 scans across eight heterogeneous datasets.
Cross-domain transfer of remote-sensing HSI foundation models improves proximal sensing semantic segmentation over in-domain training and narrows the gap to cross-modality methods on the HS3-Bench benchmark.
VSLP infers dense segmentations from global label proportions via a pre-trained transformer for initial confidence maps followed by variational optimization using Wasserstein fidelity and a learned regularizer, outperforming prior weakly supervised methods on histopathology datasets.
The ICPR 2026 LRLPR competition on real low-quality license plate images drew 99 valid submissions, with the winning team reaching 82.13% recognition rate and four teams exceeding 80%.
A tornado outbreak with simultaneous tornadic supercells occurred in the Philippines within an easterly severe weather regime, documented as the first known instance there.
FlowForge predicts flow fields via staged local updates with a shared lightweight predictor, matching or exceeding baselines in accuracy while improving robustness to noise and reducing latency.
PSIRNet produces diagnostic-quality free-breathing PSIR LGE cardiac MRI from a single interleaved IR/PD acquisition over two heartbeats using a physics-guided deep learning network trained on over 800,000 slices.
CATMIL augments nnU-Net with component-adaptive Tversky and MIL-based lesion supervision to raise Dice scores, small-lesion recall, and error control on the MSLesSeg dataset.
RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
RSEdit adapts off-the-shelf text-to-image models into a collection of editing systems that follow text instructions while keeping geospatial structure intact in remote sensing images.
ASTERIS, a self-supervised spatiotemporal denoising algorithm, improves astronomical detection limits by 1 magnitude at 90% completeness while identifying three times more redshift >9 galaxy candidates in JWST images.
citing papers explorer
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Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation
STREAM decouples text and music conditioning in a diffusion transformer via AdaLN for structure and BEAM for beats, plus new Motorica++ dataset and editability metrics, claiming SOTA music alignment with preserved semantics.
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GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products
GPROF-IR is a CNN-based retrieval that uses temporal context in geostationary IR observations to produce precipitation estimates with lower error than prior IR methods and climatological consistency with PMW retrievals for integration into IMERG V08.
-
AttentionBender: Manipulating Cross-Attention in Video Diffusion Transformers as a Creative Probe
AttentionBender applies 2D transforms to cross-attention maps in video diffusion transformers, producing distributed distortions and glitch aesthetics that reveal entangled attention mechanisms while serving as both an XAI probe and creative tool.
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Differentiable Surrogate for Detector Simulation and Design with Diffusion Models
A LoRA-adapted conditional diffusion surrogate for electromagnetic calorimeter showers matches key observables within 2% RMSE and reproduces directional trends in design-utility gradients.
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OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations
OOD-SEG reframes multi-class segmentation from sparse positive-only annotations as pixel-wise positive-unlabelled learning solved by integrating out-of-distribution detection techniques, with a proposed cross-validation evaluation on surgical imaging datasets.
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Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Proposes a cyclic 2.5D perceptual loss with manufacturer SUVR standardization for T1w MRI to tau PET synthesis, reporting improved regional agreement on ADNI and SCAN cohorts across U-Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix.
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DeepMine-Mamba: Mitigating Information Dilution in Mamba-Based State Space Models for Document Image Binarization
DeepMine-Mamba adds an Anti-Dilution Gate to Mamba-based models to counteract feature dilution in document binarization and reports competitive FM and Fps scores on DIBCO benchmarks under leave-one-year-out evaluation.
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Direct High-Magnetic-Field Coupling to Stripe Order in a Cuprate Superconductor
High magnetic fields directly enhance the amplitude and correlation length of stripe order in a cuprate superconductor far above the vortex melting transition, indicating a coupling mechanism independent of superconductivity suppression.
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Multi-Task Crack Foundation Model for Engineering-Reliable Crack Representation and Topology Preservation in Civil Infrastructure
CrackGeoFM is a multi-task framework that adapts a frozen visual foundation model with FCEM, CFAM, and SMTD modules for crack mask prediction, skeleton reconstruction, and uncertainty estimation, reporting SOTA results across 20 datasets including few-shot settings.
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XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity
A Jacobian sensitivity curve computed at initialization identifies the narrowest U-Net configuration that avoids performance collapse, matching nnU-Net accuracy with 400-1600x fewer parameters on six medical datasets.
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Spectral Lens: Activation and Gradient Spectra as Diagnostics of LLM Optimization
Spectral analysis of activations and gradients provides new diagnostics that link batch size to representation geometry, early covariance tails to token efficiency, and spectral shifts to learning dynamics in decoder-only LLMs, backed by a mechanistic model.
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A Robust Foundation Model for Conservation Laws: Injecting Context into Flux Neural Operators via Recurrent Vision Transformers
A recurrent Vision Transformer hypernetwork injects context into Flux Neural Operators to infer and solve unseen conservation laws while preserving robustness and long-time stability.
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SIAM: Head and Brain MRI Segmentation from Few High-Quality Templates via Synthetic Training
SIAM achieves state-of-the-art whole-head MRI segmentation of 16 structures including extra-cerebral tissues by training on synthetic data from just six manual templates, matching or exceeding prior methods on 301 scans across eight heterogeneous datasets.
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Cross-Domain Transfer of Hyperspectral Foundation Models
Cross-domain transfer of remote-sensing HSI foundation models improves proximal sensing semantic segmentation over in-domain training and narrows the gap to cross-modality methods on the HS3-Bench benchmark.
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Semantic Segmentation for Histopathology using Learned Regularization based on Global Proportions
VSLP infers dense segmentations from global label proportions via a pre-trained transformer for initial confidence maps followed by variational optimization using Wasserstein fidelity and a learned regularizer, outperforming prior weakly supervised methods on histopathology datasets.
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Localized Tornado Outbreak at the Upstream of a Tropical Easterly Wave in Camarines Norte, Philippines (13 September 2025)
A tornado outbreak with simultaneous tornadic supercells occurred in the Philippines within an easterly severe weather regime, documented as the first known instance there.
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FlowForge: A Staged Local Rollout Engine for Flow-Field Prediction
FlowForge predicts flow fields via staged local updates with a shared lightweight predictor, matching or exceeding baselines in accuracy while improving robustness to noise and reducing latency.
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Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI
CATMIL augments nnU-Net with component-adaptive Tversky and MIL-based lesion supervision to raise Dice scores, small-lesion recall, and error control on the MSLesSeg dataset.
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RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy
RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
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RSEdit: Text-Guided Image Editing for Remote Sensing
RSEdit adapts off-the-shelf text-to-image models into a collection of editing systems that follow text instructions while keeping geospatial structure intact in remote sensing images.
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Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
ASTERIS, a self-supervised spatiotemporal denoising algorithm, improves astronomical detection limits by 1 magnitude at 90% completeness while identifying three times more redshift >9 galaxy candidates in JWST images.
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Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformers
DiffuMeta uses diffusion transformers and algebraic language representations to generate diverse 3D shell metamaterials with targeted stress-strain responses under large deformations including buckling and contact.
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MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation
MLFFM-SegDiff adds a multi-level feature fusion module and dual-path encoder to a diffusion U-Net, reporting improved Jaccard (0.8546) and Dice (0.9207) scores over baselines on three skin lesion datasets.
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Test-Time Adaptation in Optical Coherence Tomography Using Trajectory-Aligned Time-Independent Flow
Flow-matching TTA with histogram matching to synthetic reference trajectories and time-independent flow achieves SOTA segmentation of AMD biomarkers in OCT.
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SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology
SegTME-UNI2 pairs a UNI2-based dual-head segmentation model trained via progressive pseudo-labeling with an LLM to produce multiclass cell maps and narrative TME descriptions from H&E images.
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Reliability of Probabilistic Emulation of Physical Systems
CRPS-trained ensembles achieve better uncertainty reliability and speed than latent generative models for probabilistic emulation of 2D physical systems.
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Physics-informed neural networks for quantitative assessment of cancellous bone microstructure from photoacoustic signals
Biot-PINN embeds Biot poroelasticity into a neural network to decode photoacoustic signals for cancellous bone microstructure grading at 97% accuracy.
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Observation-Guided Neural Surrogate Learning for Scientific Simulation Emulation: A Single-Gauge Flood-Inundation Proof of Concept
An EnsCGP coarse surrogate plus U-Net-ASPP corrector emulates LISFLOOD-FP flood depths on a 256x256 grid around one Chicago gauge, achieving R² ≈ 0.99 and MAE < 0.01 m on held-out events while matching the gauge depth at that single pixel.
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Weighted Knowledge Distillation for Semi-Supervised Segmentation of Maxillary Sinus in Panoramic X-ray Images
A semi-supervised framework using weighted knowledge distillation and SinusCycle-GAN refinement achieves 96.35% Dice score for maxillary sinus segmentation in panoramic X-rays from 2,511 patients.
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Training-inference input alignment outweighs framework choice in longitudinal retinal image prediction
Training-inference input alignment outweighs framework choice for longitudinal retinal image prediction, with deterministic regression matching complex models when acquisition variability dominates disease progression.
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Physics Priors Offer Useful Accuracy-Carbon Trade-Offs in Spatio-Temporal Forecasting
Stronger physics priors in neural networks for spatio-temporal shear flow forecasting yield substantially lower training carbon footprints than weak or no priors, though inference savings are less consistent.
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Do We Really Need Diffusion? A Fast U-Net for Paired Medical Image Translation
Lightweight U-Net outperforms DDPM on T2w-to-MRI-SFF translation (r=0.975 vs 0.962, MAE=0.014 vs 0.019) with 208x faster inference on 230k paired images from NAKO.
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Patient-Level Diagnosis of Acute Myeloid Leukemia via Deep Learning Analysis of Bone Marrow Smear
YOLO segmentation plus EfficientNet classification aggregates cell predictions to patient-level CBLC ratios, reporting weighted F1 scores of 0.87-0.91 on three external center cohorts from 89 patients.
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$\mu$-FlowNet: A Deep Learning Approach for Mapping Flow Fields in Irregular Microchannels Using an Attention-based U-Net Encoder-Decoder Architecture
μ-FlowNet applies an attention U-Net to map flow fields in irregular microchannels, reporting dice score 0.9317 and IoU 0.8731 on test data while outperforming standard U-Net and T-Net.
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Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning
MAML with auxiliary cavity tasks and boundary loss improves 5-shot LA wall segmentation over standard fine-tuning (DSC 0.54 vs 0.48) and nears fully supervised performance at 20 shots.
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Predicting parameters of a model cuprate superconductor using machine learning
An adapted U-Net model trained on mean-field phase diagrams accurately predicts Hamiltonian parameters for a cuprate superconductor when validated on Monte Carlo simulation data.
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Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training
Densely connected U-Net with GAN-guided training and perceptual loss corrects respiratory motion artifacts in abdominal MRI.
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Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset
Pre-training nnU-Net and MedNeXt on BraTS 2025 data then fine-tuning on BraTS-Africa with added topology refinement yields NSD scores of 0.810, 0.829, and 0.895 for SNFH, NETC, and ET.