Latest AI and machine learning research in identifying and reporting dependent adult abuse for healthcare professionals.
Objective: How structured clinical features and cluster-semantic embeddings interact under self-distillation in EHR prediction models is unknown. Existing approaches treat these sources separately (gradient-boosted trees exploit tabular features while sequence models process text), and their interaction under self-distillation regularisation remains uncharacterised. We introduce the Narrative Velo...
Cancer screening is a reasoning task. A radiologist observes findings, compares them to prior scans, integrates clinical context, and reaches a diagnostic conclusion confirmed by pathology. We present RadThinking, a Visual Question Answering (VQA) dataset that makes this reasoning explicit and trainable. RadThinking releases VQA pairs at three difficulty tiers. Foundation VQAs are atomic perceptio...
Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitu...
Despite significant progress, RGB-based trackers remain vulnerable to challenging imaging conditions, such as low illumination and fast motion. Event ...
Recent advances in Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have made it standard practice to reconstruct 3D scenes from multi-v...
Antiphospholipid syndrome (APS) lacks targeted therapies beyond anticoagulation, and its molecular heterogeneity remains poorly characterized. We empl...
Despite significant progress in Unified Multimodal Retrieval (UMR) powered by Large Multimodal Models (LMMs), existing embedding methods primarily foc...
Semantic segmentation of multi-modal remote sensing imagery plays a pivotal role in land use/land cover (LULC) mapping, environmental monitoring, and ...
With the development of deep learning, ViT-based stereo matching methods have made significant progress due to their remarkable robustness and zero-sh...
Multimodal Large Language Models (MLLMs) have been increasingly used as automatic evaluators-a paradigm known as MLLM-as-a-Judge. However, their relia...
Background Randomized controlled trials (RCTs) often have incomplete methods reporting despite widespread adoption of the CONSORT guideline. The edito...
The capacity to generate high-dimensional biological datasets has outpaced the ability to interpret them. Technologies such as phage immunoprecipitati...
Wideband spectrum sensing for low-altitude monitoring is critical yet challenging due to heterogeneous protocols,large bandwidths, and non-stationary ...
Cyclic peptides are recognized as versatile scaffolds for therapeutic and functional applications due to their structural stability and resistance to ...
Ship detection for navigation is a fundamental perception task in intelligent waterway transportation systems. However, existing public ship detection...
Recent semi-dense image matching methods have achieved remarkable success, but two long-standing issues still impair their performance. At the coarse ...
The deep integration of communication with intelligence and sensing, as a defining vision of 6G, renders environment-aware channel prediction a key en...
Background: The integration of artificial intelligence (AI) into clinical practice holds transformative potential for healthcare in West Africa, but s...
The rapid advancement of AI research automation systems--including AI Scientist, data-to-paper, and Agent Laboratory--has demonstrated the potential f...
Background: The FDA Adverse Event Reporting System (FAERS) is a critical pillar of post-marketing pharmacovigilance; however, its utility is constrain...