Latest AI and machine learning research in clinical trials for healthcare professionals.
Online safety fault diagnosis is essential for lithium-ion batteries in electric vehicles(EVs), particularly under complex and rare safety-critical conditions in real-world operation. In this work, we develop an online battery fault diagnosis network based on a deep anomaly detection framework combining kernel one-class classification and minimum-volume estimation. Mechanical constraints and spike...
Text-to-image (T2I) models such as Stable Diffusion and DALLE remain susceptible to generating harmful or Not-Safe-For-Work (NSFW) content under jailbreak attacks despite deployed safety filters. Existing jailbreak attacks either rely on proxy-loss optimization instead of the true end-to-end objective, or depend on large-scale and costly RL-trained generators. Motivated by these limitations, we pr...
This paper presents ARYA, a composable, physics-constrained, deterministic world model architecture built on five foundational principles: nano models...
Background: Cardiovascular disease (CVD) prevention is limited by the major challenge of low long-term adherence to effective lifestyle regimens. Arte...
Systems powered by large language models are widely used for health information and advice, yet robust evidence for their safety and effectiveness in ...
Frontier language models are widely used for health-related queries, yet aggregate benchmark scores do not capture safety implications of errors. We a...
Background: The administrative burden of clinical documentation is a recognised contributor to clinician burnout and diminished care quality. Ambient ...
Small airways are the primary sites of airflow obstruction in chronic obstructive pulmonary disease. Effective delivery of aerosolized drug particles ...
Micromobility is a growing mode of transportation, raising new challenges for traffic safety and planning due to increased interactions in areas where...
Background: Advances in medicine depend on analyzing large and complex data sources, but discovery is partly constrained by the limited time and domai...
Unified Multimodal Models (UMMs) offer powerful cross-modality capabilities but introduce new safety risks not observed in single-task models. Despite...
Iris presentation attack detection (PAD) is critical for secure biometric deployments, yet developing specialized models faces significant practical b...
We present KidsNanny, a two-stage multimodal content moderation architecture for child safety. Stage 1 combines a vision transformer (ViT) with an obj...
Synapses are the fundamental units of neural computation, yet quantifying their organization across circuit-level scales remains a critical bottleneck...
Multi-modal Large Language Models (MLLMs) have achieved remarkable performance across a wide range of visual reasoning tasks, yet their vulnerability ...
Automated radiology report generation from 3D CT volumes often suffers from incomplete pathology coverage. We provide empirical evidence that this lim...
The progressive automation of transport promises to enhance safety and sustainability through shared mobility. Like other vehicles and road users, and...
Background and Objective: Increasing screening volumes, combined with global shortage of radiologists and a high proportion of normal mammograms, chal...
Adversarial patches are physically realizable localized noise, which are able to hijack Vision Transformers (ViT) self-attention, pulling focus toward...
The rapid evolution of embodied agents has accelerated the deployment of household robots in real-world environments. However, unlike structured indus...