Results 51 to 60 of about 16,806 (262)
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source
The present study investigates recycling of NiTi shape memory alloys via vacuum induction melting. An ingot was synthesized from elemental Ni and Ti and subjected to three subsequent remelting cycles. Remelting increases process durations and impurity levels and adversely affects microstructures and functional properties.
Sakia Sophia Noorzayee +7 more
wiley +1 more source
Zero-Shot Learning for Sustainable Municipal Waste Classification
Automated waste classification is an essential step toward efficient recycling and waste management. Traditional deep learning models, such as convolutional neural networks, rely on extensive labeled datasets to achieve high accuracy.
Dishant Mewada +5 more
doaj +1 more source
Evolutionary Generalized Zero-Shot Learning
IJCAI2024
Dubing Chen +4 more
openaire +4 more sources
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari +5 more
wiley +1 more source
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source
Creativity Inspired Zero-Shot Learning [PDF]
This paper was published at the International Conference on Computer Vision 2019, Seoul, South Korea, http://openaccess.thecvf.com/content_ICCV_2019/papers/Elhoseiny_Creativity_Inspired_Zero-Shot_Learning_ICCV_2019_paper ...
Mohamed Elhoseiny, Mohamed Elfeki
openaire +3 more sources
Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter +10 more
wiley +1 more source
Zero-Shot Action Recognition with Three-Stream Graph Convolutional Networks
Large datasets are often used to improve the accuracy of action recognition. However, very large datasets are problematic as, for example, the annotation of large datasets is labor-intensive.
Nan Wu, Kazuhiko Kawamoto
doaj +1 more source
Zero-shot Decentralized Federated Learning
CLIP has revolutionized zero-shot learning by enabling task generalization without fine-tuning. While prompting techniques like CoOp and CoCoOp enhance CLIP's adaptability, their effectiveness in Federated Learning (FL) remains an open challenge. Existing federated prompt learning approaches, such as FedCoOp and FedTPG, improve performance but face ...
Masano A. +4 more
openaire +4 more sources

