Results 111 to 120 of about 24,720 (203)

Practical Consequences of the Bias in the Laplace Approximation to Marginal Likelihood for Hierarchical Models. [PDF]

Entropy (Basel)
Lele SR, Glen CG, Ponciano JM.
europepmc   +1 more source

Machine Learning‐Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of image quality metrics in combination with machine learning enables automatic image quality assessment for fluorescence microscopy images. The method can be integrated into the experimental pipeline for optical microscopy and utilized to classify artifacts in experimental images and to build quality rankings with a reference‐free approach ...
Elena Corbetta, Thomas Bocklitz
wiley   +1 more source

Stochastic step-wise feature selection for Exponential Random Graph Models (ERGMs). [PDF]

PLoS One
El-Zaatari H, Yu F, Kosorok MR.
europepmc   +1 more source

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

Strength and weakness of disease-induced herd immunity in networks. [PDF]

Proc Natl Acad Sci U S A
Hiraoka T, Ghadiri Z, Rizi AK, Kivelä M, Saramäki J.   +4 more
europepmc   +1 more source

Three Synchronized, Miniaturized, Wireless Inertial Measurement Unit Sensors for Automated Gait and Balance Analysis System

Advanced Intelligent Systems, EarlyView.
This study presents a compact, three IMU wearable system that enables accurate motion capture and robust gait‐feature extraction, thereby supporting reliable machine learning‐based balance evaluation. Accurate assessment of balance is critical for fall prevention and targeted rehabilitation, particularly in older adults and individuals with ...
Seok‐Hoon Choi, Ki‐Hoon Kim, Heung Jin Park, Yuna Kim, Hyunjin Lee, Yu Bin Lee, Youn Joung Kang, Sungchul Huh, Min‐Ho Seo   +8 more
wiley   +1 more source

FoxP3 forms a head-to-head dimer in vivo and stabilizes its multimerization on adjacent microsatellites. [PDF]

Cell Rep
Leng F, Clark R, Zhang W, Viennet T, Wang C, Arthanari H, Wang X, Hur S.   +7 more
europepmc   +1 more source

Soft Robotic Sim2Real via Conditional Flow Matching

Advanced Intelligent Systems, EarlyView.
A new framework based on conditional flow matching addresses the persistent Sim2Real gap in soft robotics. By learning a conditional probability path, the model directly transforms inaccurate simulation data to match physical reality, successfully capturing complex phenomena like hysteresis.
Ge Shi, Xing Wang, André Farinha, Joshua Pinskier, Xing Shen, Richard Scalzo, David Howard   +6 more
wiley   +1 more source

Partial differential equations in data science. [PDF]

Philos Trans A Math Phys Eng Sci
Bertozzi AL, Drenska N, Latz J, Thorpe M.   +3 more
europepmc   +1 more source

Bridging High‐Fidelity Simulations and Physics‐Based Learning using a Surrogate Model for Soft Robot Control

Advanced Intelligent Systems, EarlyView.
A surrogate‐model‐based framework is proposed for combining high‐fidelity finite element method and efficient physics simulations to enable fast, accurate soft robot simulation for reinforcement learning, validated through sim‐to‐real experiments. Soft robotics holds immense promise for applications requiring adaptability and compliant interactions ...
Taehwa Hong, Jungjae Lee, Byung‐Hyun Song, Yong‐Lae Park   +3 more
wiley   +1 more source