The Outline of Deception: Physical Adversarial Attacks on Traffic Signs Using Edge Patches

A new operator-learning framework has been introduced to enhance Earth-system model (ESM) predictions by addressing limitations such as coarse resolution and uncertain initial states. This framework utilizes a U-Net backbone to develop two architectures, Inception U-Net and a multi-scale network, which improve bias-correction tendencies during model integration. The operators were trained on E3SM simulations aligned with ERA5 reanalysis data.

A new hybrid model named TransUNet-GradCAM has been developed for the automated segmentation of diabetic foot ulcers (DFUs), integrating the U-Net architecture with Vision Transformers to enhance feature extraction and spatial resolution. This model addresses challenges posed by the heterogeneous appearance and irregular morphology of DFUs in clinical images, improving diagnostic accuracy and therapeutic planning.

A recent study has introduced an ultra-strong gradient diffusion MRI technique combined with self-supervised learning to enhance the characterization of prostate cancer. This approach leverages the VERDICT framework to provide detailed microstructural insights, overcoming limitations of traditional imaging methods that often lack specificity to histological features.

Recent advancements in AI multimodal geospatial foundation models, particularly ESA-IBM's TerraMind, have been leveraged for enhanced near-real-time flood mapping globally. This development comes in light of extreme flood events impacting communities across five continents in 2024, the warmest year on record. The study fine-tunes TerraMind using FloodsNet, a multimodal dataset combining Sentinel-1 and Sentinel-2 imagery for 85 flood events worldwide.

A new study introduces Multifractal Recalibration methods for enhancing neural networks used in medical imaging segmentation, specifically within a U-Net framework. This approach addresses limitations in existing multifractal techniques that often rely on heavy pooling, thereby improving the statistical representation of encoder embeddings through channel-attention functions.

MegaSR has been introduced as a novel approach to enhance text-to-image (T2I) models for real-world image super-resolution (Real-ISR), addressing critical issues such as fine detail deficiency and edge ambiguity that hinder accurate image reconstruction. This method integrates customized semantics and expressive guidance to improve the semantic richness and structural consistency of generated images.