Results 151 to 160 of about 8,311,668 (271)

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

From structure to sequence: A multinomial processing tree model of syntactic encoding. [PDF]

Psychon Bull Rev
Yeaton JD, Walker GM, Fahey D, Matchin W, Fridriksson J, Hickok G.   +5 more
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

First-Line Maintenance Therapy Patterns in BRCA Wild-Type, HRD-Negative or Biomarker-Unknown Advanced Epithelial Ovarian Cancer: A Five-Country European Real-World Study by APLUSA. [PDF]

Cancers (Basel)
Uccello M, Boussios S, Maï C, Worton H, Bazire A, de Buhren B.   +5 more
europepmc   +1 more source

Modelling count data using the logratio-normal-multinomial distribution

The logratio-normal-multinomial distribution is a count data model resulting from compounding a multinomial distribution for the counts with a multivariate logratio-normal distribution for the multinomial event probabilities. However, the logratio-normal-
Comas-Cufí, Marc, Mateu-Figueras, Glòria, Martín-Fernández, Josep-Antoni, Palarea-Albaladejo, Javier   +3 more
core  

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting

Advanced Intelligent Discovery, EarlyView.
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto, Gabriel Villarrubia Gonzalez, Ademir Nied, Laio Oriel Seman, Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon   +5 more
wiley   +1 more source

How Endpoint Definitions Shape Epidemiologic Inference on Tuberculosis Treatment Outcomes: A Secondary Analysis from Lima, Peru. [PDF]

Infect Dis Rep
Vera-Ponce VJ, Ballena-Caicedo J, Cervera Vasquez NJ, García-Rodríguez MD, Yumpo Ynga JA, Zuzunaga-Montoya FE.   +5 more
europepmc   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Non-negative matrix factorization algorithms generally improve topic model fits. [PDF]

Stat Comput
Carbonetto P, Sarkar A, Wang Z, Stephens M.   +3 more
europepmc   +1 more source