Leveraging Context for Perceptual Prediction Using Word Embeddings. [PDF]
Carter GA, Keller F, Hoffman P.
europepmc +1 more source
Combining word embeddings to extract chemical and drug entities in biomedical literature. [PDF]
López-Úbeda P +3 more
europepmc +1 more source
This study explores how information processing is distributed between brains and bodies through a codesign approach. Using the “backpropagation through soft body” framework, brain–body coupling agents are developed and analyzed across several tasks in which output is generated through the agents’ physical dynamics.
Hiroki Tomioka +3 more
wiley +1 more source
Predicting drug-gene relations via analogy tasks with word embeddings. [PDF]
Yamagiwa H +8 more
europepmc +1 more source
An enhanced universal gripper combining rigid mechanics with self‐adaptable fingers is presented for industrial automation. The novel six‐bar linkage with integrated compliant pad eliminates mechanical interference while enabling passive shape adaptation.
Muhammad Usman Khalid +7 more
wiley +1 more source
Leveraging word embeddings to enhance co-occurrence networks: A statistical analysis. [PDF]
Amancio DR, Machicao J, Quispe LVC.
europepmc +1 more source
Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesaurus Using Siamese Networks. [PDF]
Bajaj G +7 more
europepmc +1 more source
Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki +2 more
wiley +1 more source
Back-translation effects on static and contextual word embeddings for topic classification embedding in classification tasks. [PDF]
Držík D, Kelebercová L.
europepmc +1 more source
Negative Associations in Word Embeddings Predict Anti-black Bias across Regions-but Only via Name Frequency. [PDF]
van Loon A +3 more
europepmc +1 more source

