Results 151 to 160 of about 1,001,459 (337)
Machine learning code snippets semantic classification
Valeriy Berezovskiy +3 more
openalex +1 more source
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian +12 more
wiley +1 more source
Pipeline corrosion has significant impacts on the human, economic, and natural environment. To help better detect and prevent it over time, in this paper, we propose a multivariate approach using machine learning.
Kalidou Moussa Sow, Nadia Ghazzali
doaj +1 more source
Semantically predictable input streams impede gaze-orientation to surprising locations
Giuseppe Notaro, Uri Hasson
openalex +2 more sources
$S^3$Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data [PDF]
Bin Cheng +4 more
openalex +1 more source
Texture based Prototypical Network for Few-Shot Semantic Segmentation of Forest Cover: Generalizing for Different Geographical Regions [PDF]
P Gokul, Ujjwal Verma
openalex +1 more source
The Necessity of Dynamic Workflow Managers for Advancing Self‐Driving Labs and Optimizers
We assess the maturity and integration readiness of key methodologies for Materials Acceleration Platforms, highlighting the need for dynamic workflow managers. Demonstrating this, we integrate PerQueue into a color‐mixing robot, showing how flexible orchestration improves coordination and optimization.
Simon K. Steensen +6 more
wiley +1 more source
THE MOST IMPORTANT STRUCTURAL AND SEMANTIC PROPERTIES OF THE DEFINITE ARTICLE
Hafıda EL AMRINI
openalex +1 more source
This study presents an automated system integrating a capillary force gripper and machine learning‐based object detection for sorting and placing submillimeter objects. The system achieved stable and simultaneous manipulation of four object types, with an average task time of 86.0 seconds and a positioning error of 157 ± 84 µm, highlighting its ...
Satoshi Ando +4 more
wiley +1 more source
Calibrated Interpretation: Confidence Estimation in Semantic Parsing [PDF]
Elias Stengel-Eskin, Benjamin Van Durme
openalex +1 more source

