Results 151 to 160 of about 343,488 (306)

RevieWeaver: Weaving Together Review Insights by Leveraging LLMs and Semantic Similarity [PDF]

Jiban Adhikary, Mohammad Alqudah, Arun Palghat Udayashankar   +2 more
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

Advanced Intelligent Discovery, EarlyView.
Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing, He Wang, Yangyang Sun, Xiaoquan Su, Shunyao Wu   +4 more
wiley   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
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, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Deep Self-Supervised Hashing With Fine-Grained Similarity Mining for Cross-Modal Retrieval

IEEE Access
With the efficiency of storage and retrieval speed, the hashing methods have attracted a lot of attention for cross-modal retrieval applications. In contrast to traditional cross-modal hashing by using handcrafted features, deep cross-modal hashing ...
Lijun Han, Renlin Wang, Chunlei Chen, Huihui Zhang, Yujie Zhang, Wenfeng Zhang   +5 more
doaj   +1 more source

Sensorimotor distance: A grounded measure of semantic similarity for 800 million concept pairs. [PDF]

Behav Res Methods, 2023
Wingfield C, Connell L.
europepmc   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

A semantic similarity analysis of multiple English translations of The Analects: Based on a natural language processing algorithm. [PDF]

Front Psychol, 2022
Yang L, Zhou G.
europepmc   +1 more source

Semantic assessment of similarity between raster elevation datasets

, 2012
Marco Moreno-Ibarra, Miguel Torres-Ruiz, Rolando Quintero, Giovanni Guzmán, Rolando Menchaca-Méndez   +4 more
openalex   +2 more sources

TATO: Leveraging on Multiple Strategies for Semantic Textual Similarity [PDF]

, 2015
Tu Thanh Vu, Quan Hung Tran, Son Bao Pham   +2 more
openalex   +1 more source