Results 81 to 90 of about 156,620 (328)

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

open access: yesAdvanced 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   +6 more
wiley   +1 more source

Tuning syntactically enhanced word alignment for statistical machine translation [PDF]

open access: yes, 2009
We introduce a syntactically enhanced word alignment model that is more flexible than state-of-the-art generative word alignment models and can be tuned according to different end tasks.
Lambert, Patrik, Ma, Yanjun, Way, Andy
core   +3 more sources

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

open access: yesAdvanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang   +4 more
wiley   +1 more source

Advanced Experiment Design Strategies for Drug Development

open access: yesAdvanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang   +3 more
wiley   +1 more source

Combination Strategies for Semantic Role Labeling

open access: yes, 2011
This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers.
Carreras, X.   +3 more
core   +1 more source

Machine Learning‐Based Standard Compact Model Binning Parameter Extraction Methodology for Integrated Circuit Design of Next‐Generation Semiconductor Devices

open access: yesAdvanced Intelligent Systems, EarlyView.
This study presents a neural network‐based methodology for Berkeley Short‐Channel IGFET Model–Common Multi‐Gate parameter extraction of gate‐all‐around field effect transistors, integrating binning adaptive sampling and transformer neural networks to efficiently capture current–voltage and capacitance–voltage characteristics.
Jaeweon Kang   +4 more
wiley   +1 more source

A Generalizable Transformer Framework for Gene Regulatory Network Inference from Single‐Cell Transcriptomes

open access: yesAdvanced Intelligent Systems, EarlyView.
FTGRN introduces an LLM‐enhanced framework for gene regulatory network inference through a two‐stage workflow. It combines a Transformer‐based model, pretrained on GPT‐4 derived gene embeddings and regulatory knowledge, with a fine‐tuning stage utilizing single‐cell RNA‐seq data.
Guangzheng Weng   +7 more
wiley   +1 more source

SYNTAX Score in Patients with High Computed Tomography Coronary Calcium Score

open access: yesJournal of Clinical Imaging Science, 2016
Objectives: To study the conventional coronary angiogram ( CA) findings in patients with high coronary calcium on multidetector computed tomogram. Materials and Methods: Fifty patients with coronary calcium high enough in its extent and location to interfere with the interpretation of a contrast-filled coronary artery for a significant lesion were ...
Hegde, Madhav, Rajendran, Ravindran
openaire   +2 more sources

AI Guided Protein Design for Next‐Generation Autogenic Engineered Living Materials

open access: yesAdvanced Intelligent Systems, EarlyView.
Autogenic engineered living materials (ELMs) integrate biology and materials science to create self‐regenerating and self‐healing materials. This perspective highlights emerging strategies in protein engineering and AI‐guided de novo design to expand the capabilities of autogenic ELMs.
Hoda M. Hammad, Anna M. Duraj‐Thatte
wiley   +1 more source

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