This paper presents features for quantitatively diagnosing the progression of gastric atrophy from stomach X-ray images, aiming to support mass screening for gastric cancer. Specifically, the features focus on the gastric areae shadow pattern, which appears as a mesh-like and low-contrast shadow that becomes prominent in cases of moderate to severe atrophy. In moderate atrophy, this pattern appears in the lower stomach region and gradually extends upward as the condition progresses. To capture this trend, the stomach area is divided into upper and lower regions, and the features are extracted by measuring the area occupied by the gastric areae shadow pattern in each region. An evaluation conducted on 157 gastric X-ray images demonstrated that the proposed features increased in correlation with atrophy progression and could effectively distinguish between normal and abnormal cases without reliance on specific classifiers.
Multi-Query Person Retrieval (mQPRS) improves security by enabling the retrieval of multiple individuals in various scenarios while providing real-time situational awareness on edge devices. This paper introduces mQPRS using soft biometrics on the NVIDIA Jetson ORIN AGX platform for the first time. It employs YOLOv8s for person detection and proposes the DensePAR model for recognising person attributes, alongside a new ranking strategy. A low-rank approximation is applied to the DensePAR model to manage computational limits on edge devices. An ablation study assesses the effects of model compression on performance using the GPU and Nvidia Jetson Orin platforms. The system achieves a 74
This work explores a state-of-the-art foundation vision model, DINOv3 ViT-B/16, to the domain of musculoskeletal (MSK) radiographs using self-supervised learning. Unlike prior work focused on supervised pipelines, we investigate whether and how domain-specific self-supervised adaptation reshapes feature representations to better support downstream anatomical region classification for registry construction. Leveraging a unified dataset of 88,303 plain radiographs across nine anatomical regions, we compare four modeling strategies, including supervised and frozen approaches, as well as self-supervised fine-tuning. Our results demonstrate that off-the-shelf DINOv3 features with a simple linear probe achieve 97.0
We present a novel framework for automatically evaluating building conditions nationwide in the United States by leveraging large language models (LLMs) and Google Street View (GSV) imagery. By fine-tuning Gemma 3 27B on a modest human-labeled dataset, our approach achieves strong alignment with human mean opinion scores (MOS), outperforming even individual raters relative to the MOS benchmark in terms of SRCC and PLCC. To enhance efficiency, we apply knowledge distillation, transferring the capability of Gemma 3 27B to a smaller Gemma 3 4B model, which attains comparable performance with a 3 × speedup. Further, we distill the knowledge into a CNN-based model (EfficientNetV2-M) and a transformer (SwinV2-B), delivering close performance while achieving a 30 × speed gain. Our framework offers a flexible and efficient solution for large-scale building condition assessment, enabling high accuracy with minimal human labeling effort.
The aurora borealis/australis is generated by the interaction between the solar wind and the Earth’s geomagnetic field. Auroral observations provide clues on the mechanisms that transfer energy from the solar wind into the terrestrial environment. However, these auroral displays, which are captured by ground-based optical instruments, are usually in the polar or arctic regions. Weather can obscure the optical instruments from capturing the auroral displays, which limits the number of days good optical data can be recorded. Recent advances in artificial intelligence have provided frameworks that can eliminate clouds on satellite images, for example. This study builds on these developments to address the problem of cloud removal in ground-based camera recordings with the goal of preserving the integrity of the auroral data. We train a deep learning model based on the U-net architecture to learn to reconstruct the aurora borealis from ground-based videos. To manufacture training data, we generate synthetic clouds using Perlin noise. Then, we combine an image with a clear sky with these synthetic clouds to generate a “cloudy” image. The synthetic clouds interlaced with an image frame of a clear sky appear similar to days with real clouds. The model is trained to remove the synthetic clouds. When given real cloudy data, the model effectively extracts auroral features from cloudy days: the filtered image on a real day features fewer clouds with a clearer and more prominent auroral structure when compared to the original image.
