Results 131 to 140 of about 155,742 (245)

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

LIO-CSI: LiDAR inertial odometry with loop closure combined with semantic information. [PDF]

PLoS One, 2021
Wang G, Gao S, Ding H, Zhang H, Cai H.
europepmc   +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

Semantic Information Theory and Applications. [PDF]

Entropy (Basel)
Tao M, Niu K, Wu Y.
europepmc   +1 more source

Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion. [PDF]

BMC Med Inform Decis Mak, 2021
Lan Y, He S, Liu K, Zeng X, Liu S, Zhao J.   +5 more
europepmc   +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

Parafoveal Processing of Orthographic, Phonological, and Semantic Information from Chinese Characters at a Distant Position: A Parafoveal Priming Study. [PDF]

Behav Sci (Basel)
Yuan X, Li S, Yan G.
europepmc   +1 more source

The Neural Underpinnings of Processing Newly Taught Semantic Information: The Role of Retrieval Practice. [PDF]

J Speech Lang Hear Res, 2021
Haebig E, Leonard LB, Deevy P, Schumaker J, Karpicke JD, Weber C.   +5 more
europepmc   +1 more source

The Necessity of Dynamic Workflow Managers for Advancing Self‐Driving Labs and Optimizers

Advanced Intelligent Discovery, EarlyView.
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, Patrick W. V. Butler, Jesper R. Pedersen, Nis Fisker‐Bødker, Tejs Vegge, Jin Hyun Chang, Ivano E. Castelli   +6 more
wiley   +1 more source

Dynamic SLAM Dense Point Cloud Map by Fusion of Semantic Information and Bayesian Moving Probability. [PDF]

Sensors (Basel)
An Q, Li S, Wan Y, Xuan W, Chen C, Zhao B, Chen X.   +6 more
europepmc   +1 more source