Results 101 to 110 of about 103,187 (313)
Bayesian consensus tree. [PDF]
, 2015 Bayesian consensus tree from two independent analyses of a 658 bp fragment of the mitochondrial CO I gene of taxa in Temnothorax nylanderi species-group. Bayesian posterior probabilities (as percentages) are given at the nodes.
Sándor Csősz (2620960) +2 more
core +1 more source
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
Advanced Materials, EarlyView.This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Advanced Materials, EarlyView.This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao +8 more
wiley +1 more source
Balistoid Bayesian consensus tree [PDF]
, 2015 Balistoid Bayesian consensus tree based upon the supermatrix also included in this data ...
Mark W. Westneat (398189) +1 more
core +1 more source
Advanced Materials, EarlyView.We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano +5 more
wiley +1 more source
Asymptotic model selection and identifiability of directed tree models with hidden variables [PDF]
, 2010 The standard Bayesian Information Criterion (BIC) is derived under some regularity conditions which are not always satisfied by the graphical models with hidden variables.
Zwiernik, Piotr
core
Advanced Optical Materials, EarlyView.A Bayesian optimization framework identifies the ideal composition for Lu2(MoO4)3:Yb–Er–Tm phosphors with minimal experimental trials. By leveraging the host's negative thermal expansion, the material achieves remarkable thermal quenching compensation.
Reiko Furukawa +7 more
wiley +1 more source
Bayesian Dyadic Trees and Histograms for Regression
, 2017 Many machine learning tools for regression are based on recursive partitioning of the covariate space into smaller regions, where the regression function can be estimated locally. Among these, regression trees and their ensembles have demonstrated impressive empirical performance. In this work, we shed light on the machinery behind Bayesian variants of
Stéphanie van der Pas +1 more
openaire +3 more sources
Time-calibrated Bayesian tree [PDF]
, 2017 Time-calibrated Bayesian tree of Proctophyllodidae and 40 outgroups inferred in ...
Klimov, Pavel B. +5 more
core +1 more source
Muscle Control of an Extra Robotic Digit
Advanced Robotics Research, EarlyView.This study compares muscle‐ and movement‐based control for operating a supernumerary robotic thumb. While movement control performs better in the proposed tasks, muscle‐based (EMG) control promotes broader motor learning. The results highlight the promise and challenges of using biosignals for human augmentation, offering new insights into intuitive ...
Julien Russ +7 more
wiley +1 more source