Research Radar
A unified intelligence feed of emerging AI, SaaS, and architectural models extracted directly from peer-reviewed scientific literature.
HIC-YOLOv5: Improved YOLOv5 For Small Object Detection
NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration
VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation
RoCo: Dialectic Multi-Robot Collaboration with Large Language Models
Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0
Osteoporosis Prediction Using VGG16 and ResNet50
Low bone mass and structural degradation are the hallmarks of osteoporosis, a disorder that increases the risk of fractures, especially in the elderly. For prompt intervention and fracture prevention, early identification is essential. However, os...
A fully autonomous robotic ultrasound system for thyroid scanning
Abstract The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a ful...
Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
Human-Algorithmic Interaction Using a Large Language Model-Augmented Artificial Intelligence Clinical Decision Support System
User Experience Design Professionals’ Perceptions of Generative Artificial Intelligence
AI-Augmented Brainwriting: Investigating the use of LLMs in group ideation
Design Principles for Generative AI Applications
CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs
The Metacognitive Demands and Opportunities of Generative AI
Human-AI Collaboration in Thematic Analysis using ChatGPT: A User Study and Design Recommendations
Reducing the Memory Footprint of 3D Gaussian Splatting
3D Gaussian splatting provides excellent visual quality for novel view synthesis, with fast training and realtime rendering; unfortunately, the memory requirements of this method for storing and transmission are unreasonably high. We first analyze...
A survey on imbalanced learning: latest research, applications and future directions
AbstractImbalanced learning constitutes one of the most formidable challenges within data mining and machine learning. Despite continuous research advancement over the past decades, learning from data with an imbalanced class distribution remains ...
Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning
AbstractEstimating protein targets of compounds based on the similarity principle—similar molecules are likely to show comparable bioactivity—is a long-standing strategy in drug research. Having previously quantified this principle, we present her...
Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning
Abstract Background Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological a...
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