Results 101 to 110 of about 189,135 (277)

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

Indistinguishability in controlled query evaluation over prioritized description logic ontologies

Web Semantics
In this paper we study Controlled Query Evaluation (CQE), a declarative approach to privacy-preserving query answering over databases, knowledge bases, and ontologies. CQE is based on the notion of censor, which defines the answers to each query posed to
Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo   +4 more
doaj   +1 more source

Computational Argumentation: A Journey Beyond Semantics, Logic, Opinions, and Easy Tasks [PDF]

, 2018
Ivan Habernal
openalex   +1 more source

Named Entity Recognition Models for Machine Learning Interatomic Potentials: A User‐Centric Approach to Knowledge Extraction from Scientific Literature

Advanced Intelligent Discovery, EarlyView.
Named entity recognition pipeline for knowledge extraction from scientific literature. Machine learning interatomic potential (MLIP) is an emerging technique that has helped achieve molecular dynamics simulations with unprecedented balance between efficiency and accuracy. Recently, the body of MLIP literature has been growing rapidly, which propels the
Bowen Zheng, Grace X. Gu
wiley   +1 more source

Proceedings of the 13th International Conference on Computational Semantics - Student Papers [PDF]

, 2019
Kenneth Lai, James Pustejovsky, Ronja Utescher, Austin Kershaw, Miroslaw Bober, Pavlo Kapustin, Michael Kapustin, Ketki Savle, Wlodek Zadrozny, Minwoo Lee, Ariel Ekgren, Rise, Amaru Gyllensten, Magnus Sahlgren, R Alur, T Henzinger, O Kupferman, R Briggs, I Ciardelli, L Zhang, L Champollion, K Fine, J Groenendijk, M Stokhof, W Jamroga, W Van Der Hoek, M Wooldridge, A Kratzer, S Kripke, R Stalnaker, K Von Fintel, R Cooper, J Ginzburg, M Everingham, S Eslami, L Van Gool, C Williams, J Winn, A Zisserman, Y Goyal, T Khot, D Summers-Stay, D Batra, D Parikh, K Simonyan, A Zisserman, P Anderson, B Fernando, M Johnson, S Gould, R Bernardi, R Cakici, D Elliott, A Erdem, E Erdem, N Ikizler-Cinbis, F Keller, A Muscat, B Plank, D Chen, W Dolan, M Coleman, T Liau, E Dale, C, M Denkowski, A Lavie, M Hodosh, P Young, J Hockenmaier, U Jain, S Lazebnik, A Schwing, S Jarvis, J Kincaid, R Braby, M, C.-Y Lin, E Hovy, T Lin, M Maire, S Belongie, J Hays, P Perona, D Ramanan, P Dollr, C Zitnick, J Long, E Shelhamer, O Vinyals, A Toshev, S Bengio, D Erhan, K Lenc, A Vedaldi, E Denton, S Chintala, A Szlam, R Fergus, P Fischer   +99 more
openalex   +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

Fast Search Using k-d Trees with Fine Search for Spectral Data Identification

Mathematics
Spectral identification is an essential technology in various spectroscopic applications, often requiring large spectral databases. However, the reliance on large databases significantly increases computational complexity.
YoungJae Son, Tiejun Chen, Sung-June Baek   +2 more
doaj   +1 more source

An Axiomatic Semantics for Data-Parallel Computation [PDF]

, 1997
A L Stewart
openalex   +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

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