Latest AI and machine learning research in medicare for healthcare professionals.
Recovering camera parameters from images and rendering scenes from novel viewpoints have long been treated as separate tasks in computer vision and graphics. This separation breaks down when image coverage is sparse or poses are ambiguous, since each task needs what the other produces. We propose Rays as Pixels, a Video Diffusion Model (VDM) that learns a joint distribution over videos and camera ...
Small longitudinal clinical cohorts, common in maternal health, rare diseases, and early-phase trials, limit computational modeling: too few patients to train reliable models, yet too costly and slow to expand through additional enrollment. We present multiplicity-weighted Stochastic Attention (SA), a generative framework based on modern Hopfield network theory that addresses this gap. SA embeds r...
Computer-use agents hold the promise of assisting in a wide range of digital economic activities. However, current research has largely focused on sho...
Autonomous highway driving, especially for long-haul heavy trucks, requires detecting objects at long ranges beyond 500 meters to satisfy braking dist...
Biological neural networks are characterized by short average path lengths, high clustering, and modular and hierarchical architectures. These complex...
Objective: Behavioral and social factors (BSFs) substantially influence the risk, onset, and progression of Alzheimer disease and related dementias (A...
Face morphing attacks compromise biometric security by creating document images that verify against multiple identities, posing significant risks from...
Driving video generation has achieved much progress in controllability, video resolution, and length, but fails to support fine-grained object-level c...
Predicting whether someone with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is crucial in the early stages of neurodegen...
Time series forecasting is critical across finance, healthcare, and cloud computing, yet progress is constrained by a fundamental bottleneck: the scar...
Contrastive learning produces coherent semantic feature embeddings by encouraging positive samples to cluster closely while separating negative sample...
Uncertainty quantification is essential for deploying machine learning models in high-stakes domains such as scientific discovery and healthcare. Conf...
Simulation-based methods such as approximate Bayesian computation (ABC) are widely used to infer the evolutionary history of populations from molecula...
Existing Multimodal Large Language Models (MLLMs) often suffer from hallucinations in long video understanding (LVU), primarily due to the imbalance b...
Generating images conditioned on multiple visual references is critical for real-world applications such as multi-subject composition, narrative illus...
Radiance fields have emerged as powerful tools for 3D scene reconstruction. However, casual capture remains challenging due to the narrow field of vie...
Protecting patient privacy remains a fundamental barrier to scaling machine learning across healthcare institutions, where centralizing sensitive data...
Online monocular 3D reconstruction enables dense scene recovery from streaming video but remains fundamentally limited by the stability-adaptation dil...
Fraud in the health landscape is an aggravating issue, with far-reaching consequences burdening the financial stability of the health industry and thr...
Long-horizon GUI agents are a key step toward real-world deployment, yet effective interaction memory under prevailing paradigms remains under-explore...