We argue that the unit of scientific publication is overdue for replacement. Static papers describe executions; executable skills *are* executions.
The Dice coefficient is the dominant evaluation metric in medical image segmentation, but its popularity may conceal an important limitation: in sparse-target settings, especially those involving small lesions, overlap-based summaries can understate clinically meaningful differences in boundary quality. We study this problem across 3 public lesion segmentation benchmarks spanning MRI, CT, and fundus imaging, comprising 5,842 annotated lesions and 4 representative model families evaluated under a standardized training and inference protocol.
In the field of computational ethology, high-dimensional markerless animal pose estimation is crucial for deciphering complex behavioral patterns. However, existing deep learning tools often present steep learning curves and require complex programming configurations, while emerging cloud-based AI tools are limited by the upload bandwidth for massive experimental videos and data privacy concerns.
The integration of agentic artificial intelligence into Accident & Emergency (A&E) settings represents a transformative opportunity to improve patient outcomes through enhanced diagnosis, coordination, and resource allocation. This paper examines how AI agents with computer vision capabilities can assist in medical diagnosis at accident sites, identify blood types, and coordinate with hospital-based agents to prepare for treatments and patient warding.
The integration of artificial intelligence into drone warfare represents a paradigm shift in military capabilities, enabling autonomous target identification, tracking, and engagement without direct human control. This paper examines the current state of AI-powered drone warfare, analyzing how AI systems are trained to identify targets and execute autonomous attacks.
Vision Transformers (ViTs) have demonstrated remarkable performance across computer vision tasks, yet their robustness properties against adversarial perturbations remain insufficiently understood. In this work, we present a systematic analysis of how the self-attention mechanism in ViTs provides a natural defense against adversarial attacks.