Results 161 to 170 of about 7,749,372 (301)
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo +3 more
wiley +1 more source
Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach [PDF]
Hierarchical relationships between risk factors are seldom taken into account in epidemiological studies though some authors stressed the importance of doing so, and proposed a conceptual framework in which each level of the hierarchy is modeled ...
Nguefack-Tsague, Georges +1 more
core
Machine‐learning models predict myopia trajectories from 12 routine ocular variables in 40 445 children across 10 hospitals. Externally validated across school‐based screening and optical‐intervention settings, the best models achieve AUROC 0.97 for myopia status and R2 0.83 for spherical equivalent, enabling personalized risk stratification to curb ...
Yitong Zhang +14 more
wiley +1 more source
Bayesian Hierarchical Models for the integration of genomic platforms [PDF]
phd thesis on bayesian hierarchical models for the integrated analysis of genomic platforms, such as gene expression microarrays and array ...
TRENTINI, FILIPPO
core
Physics‐Guided Modeling of Temperature‐Dependent Electrical Conductivity of Functional Oxides
This complete workflow presents an automated, data‐driven screening of functional oxides based on their predicted electrical conductivity. A curated dataset is fed into TriCondNet, a charge‐transport, physics‐guided neural network that classifies an input composition's conduction mechanism and then simulates its conductivity–temperature profile.
Elias Assaad +6 more
wiley +1 more source
Data scarcity is a critical bottleneck for machine learning in applied chemistry. We show that physics‐informed descriptors enable a simple linear model to predict the deblocking temperatures of capped diisocyanates to within 11 °C from just 19 measurements, demonstrating that embedding physical insight into descriptors, rather than relying on ...
Remsha Rafiq +9 more
wiley +1 more source
Bayesian inference for Hidden Markov Model [PDF]
Hidden Markov Models can be considered an extension of mixture models, allowing for dependent observations. In a hierarchical Bayesian framework, we show how Reversible Jump Markov Chain Monte Carlo techniques can be used to estimate the parameters of ...
Luisa Scaccia, Rosella Castellano
core
A schema‐first alignment framework builds compact, executable domain‐specific language models under data scarcity. Large‐scale synthetic question‐answer generation instills domain knowledge, and a code‐centric IR‐to‐DPO pipeline aligns generation with tool‐executable syntax.
Di Wang +4 more
wiley +1 more source
Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
wiley +1 more source
Multi‐Stage Social Value Orientation‐Based Lane‐Changing Driving for Autonomous Vehicles
A multi‐stage SVO framework is developed to dynamically infer the social preferences of surrounding vehicles from human driving data. By combining offline learning, online SVO estimation, temporal intention tracking, and interaction‐aware planning, the framework enables autonomous vehicles to adapt lane‐changing decisions and trajectories to evolving ...
Nongrui Qin +4 more
wiley +1 more source

