Results 111 to 120 of about 517,396 (274)
An in situ electroplating approach for MEX 3D printing is proposed, enabling copper deposition during the fabrication of conductive polymers. The method combines a printer‐integrated plating head, ML‐based g‐code control, and stop‐and‐go printing, achieving near‐bulk copper conductivity and enabling fully embedded, assembly‐free electronic components ...
Gianluca Percoco +5 more
wiley +1 more source
Tibetan Few‐Shot Learning Model With Deep Contextualised Two‐Level Word Embeddings
Few‐shot learning is the task of identifying new text categories from a limited set of training examples. The two key challenges in few‐shot learning are insufficient understanding of new samples and imperfect modelling.
Ziyue Zhang +11 more
doaj +1 more source
Few-shot Learning in Intelligent Agriculture: A Review of Methods and Applications
Due to the high cost of data acquisition in many specific fields, such as intelligent agriculture, the available data is insufficient for the typical deep learning paradigm to show its superior performance.
Sezai Ercisli +7 more
doaj +1 more source
Magnetic Textiles: A Review of Materials, Fabrication, Properties, and Applications
Magnetic textiles (M‐textiles) are emerging as a programmable materials platform that merges magnetic matter with hierarchical textile structures. This article consolidates magnetic material classes, textile architectures, and fabrication and magnetization strategies, revealing structure–property–function relationships that govern magneto‐mechanical ...
Li Ke +3 more
wiley +1 more source
An automation interface for environmental scanning electron microscopy (ESEM) enables simultaneous, interlaced data sets via frame‐by‐frame parameter changes. Demonstrated on oscillatory hydrogen oxidation over cobalt (Co) foil, dual‐magnification imaging bridges mesoscopic to microscopic length scales, capturing alternating views of surface dynamics ...
Maurits Vuijk +7 more
wiley +1 more source
Scalable Task Planning via Large Language Models and Structured World Representations
This work efficiently combines graph‐based world representations with the commonsense knowledge in Large Language Models to enhance planning techniques for the large‐scale environments that modern robots will need to face. Planning methods often struggle with computational intractability when solving task‐level problems in large‐scale environments ...
Rodrigo Pérez‐Dattari +4 more
wiley +1 more source
Relational Generalized Few-Shot Learning
Transferring learned models to novel tasks is a challenging problem, particularly if only very few labeled examples are available. Although this few-shot learning setup has received a lot of attention recently, most proposed methods focus on discriminating novel classes only.
Shi, Xiahan +4 more
openaire +2 more sources
The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?
Research in cognitive robotics founded on principles of developmental psychology and enactive cognitive science would yield what we seek in autonomous robots: the ability to perceive its environment, learn from experience, anticipate the outcome of events, act to pursue goals, and adapt to changing circumstances without resorting to training with ...
David Vernon
wiley +1 more source
Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback
BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat +4 more
wiley +1 more source
Prompt-based learning for few-shot class-incremental learning
Few-Shot Class-Incremental Learning (FSCIL) aims to enable deep neural networks to incrementally learn new tasks from a limited number of labeled samples, while retaining knowledge of previously learned tasks, mimicking the way humans learn.
Jicheng Yuan +6 more
doaj +1 more source

