Results 61 to 70 of about 50,616 (261)

Hierarchical Bayesian Models of Subtask Learning

Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking task,
Jeromy, Anglim, Sarah K A, Wynton
openaire   +3 more sources

Mechanistically Interpretable Artificial Intelligence for Designing Oxygen Electrocatalysts

Advanced Materials, EarlyView.
Mechanistically interpretable artificial intelligence screens nearly seven million perovskite compositions and identifies key descriptors—d‐p hybridization and densification resistance—that govern oxygen electrocatalysis. The discovered BaCo0.8Nb0.1Zr0.1O3‐δ achieves a record 2.68 W cm−2 peak power density at 600°C with over 500 h of durable operation ...
Xueyu Hu, Yucun Zhou, Haoyu Li, Zheyu Luo, Nai Shi, Yong Ding, Weining Wang, Weilin Zhang, Doyeub Kim, Chanho Kim, Yoojin Ahn, Nikhil Govindarajan, Minda Zou, Zhijun Liu, Yuefeng Song, Meilin Liu   +15 more
wiley   +1 more source

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Bayesian hierarchical models combining different study types and adjusting for covariate imbalances: a simulation study to assess model performance.

PLoS ONE, 2011
BackgroundBayesian hierarchical models have been proposed to combine evidence from different types of study designs. However, when combining evidence from randomised and non-randomised controlled studies, imbalances in patient characteristics between ...
C Elizabeth McCarron, Eleanor M Pullenayegum, Lehana Thabane, Ron Goeree, Jean-Eric Tarride   +4 more
doaj   +1 more source

Modeling the probability of a batter/pitcher matchup event: A Bayesian approach. [PDF]

PLoS ONE, 2018
We develop a Bayesian hierarchical log5 model to predict the probability of a particular batter/pitcher matchup event in baseball by extending the log5 model which is widely used for describing matchup events.
Woojin Doo, Heeyoung Kim
doaj   +1 more source

On‐Chip Photonic Neural Network Architectures

Advanced Optical Materials, EarlyView.
This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong, Berkay Neseli, Seungyeon Cho, Seungsoo Lee, Yeonjun Kim, Seungyoon Choi, Hyo‐Hoon Park, Hamza Kurt   +7 more
wiley   +1 more source

A Bayesian Hierarchical Cox Model with Elastic Net Regularization for Improved Survival Prediction and Feature Selection

Mathematics
In recent years, the growing availability of large-scale data across a wide range of disciplines has created new opportunities for developing models that improve the predictive accuracy of statistical models.
Bulus I. Doroh, Kazeem A. Dauda, Rasheed K. Lamidi   +2 more
doaj   +1 more source

3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends

Advanced Robotics Research, EarlyView.
Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu, Bin Cui, Xumin Huang, Xiaoyu (Rayne) Zheng, T. Thang Vo‐Doan, Ruirui Qiao   +5 more
wiley   +1 more source

Projecting malaria elimination in Thailand using Bayesian hierarchical spatiotemporal models

Scientific Reports, 2023
Thailand has set a goal of eliminating malaria by 2024 in its national strategic plan. In this study, we used the Thailand malaria surveillance database to develop hierarchical spatiotemporal models to analyze retrospective patterns and predict ...
Chawarat Rotejanaprasert, Saranath Lawpoolsri, Patiwat Sa-angchai, Amnat Khamsiriwatchara, Chantana Padungtod, Rungrawee Tipmontree, Lynette Menezes, Jetsumon Sattabongkot, Liwang Cui, Jaranit Kaewkungwal   +9 more
doaj   +1 more source

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source