Results 181 to 190 of about 78,936 (284)

Deep learning for the change-point Cox model with current status data. [PDF]

Lifetime Data Anal
Huang Q, Feng A, Wu Q, Tong X.
europepmc   +1 more source

Bayesian Optimization of Grayscale Patterns for Layer‐Height Accuracy in Projection Multi‐Photon 3D Printing

Laser &Photonics Reviews, EarlyView.
Bayesian optimization combined with in situ quantitative phase imaging enables autonomous correction of layer‐height deviations in projection multi‐photon lithography. By jointly tuning model parameters and grayscale exposure settings, the method achieves more uniform and accurate layers within 300 prints, offering a fast, data‐efficient route to ...
Jason E. Johnson, Xianfan Xu
wiley   +1 more source

Nonparametric Causal Inference for Optogenetics: Sequential Excursion Effects for Dynamic Regimes. [PDF]

J Am Stat Assoc
Loewinger G, Levis AW, Pereira F.
europepmc   +1 more source

Structure‐Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles

Macromolecular Rapid Communications, EarlyView.
This work presents a structure‐aware graph convolutional network that models polymers as statistical ensembles to predict macroscopic properties. By combining topologically realistic graphs generated via kinetic Monte Carlo simulations with explicit molar mass distributions, the framework achieves high accuracy in classifying architectures and ...
Julian Kimmig, Yannik Köster, Timo Koswig, Punith Raviswamy, Subhash V. S. Ganti, Stefan Zechel, Christopher Kuenneth, Ulrich S. Schubert   +7 more
wiley   +1 more source

Non-Parametric Goodness-of-Fit Tests Using Tsallis Entropy Measures. [PDF]

Entropy (Basel)
Cadirci MS.
europepmc   +1 more source

Extending the hyper‐logistic model to the random setting: New theoretical results with real‐world applications

Mathematical Methods in the Applied Sciences, EarlyView.
We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point.
Juan Carlos Cortés, Ana Navarro‐Quiles, Sorina Madalina Sferle   +2 more
wiley   +1 more source

Hazard-based distributional regression via ordinary differential equations. [PDF]

Stat Methods Med Res
A Christen J, J Rubio F.
europepmc   +1 more source

On the analyzing of bifurcation properties of the one‐dimensional Mackey–Glass model by using a generalized approach

Mathematical Methods in the Applied Sciences, EarlyView.
The goal of this work is to look at how a nonlinear model describes hematopoiesis and its complexities utilizing commonly used techniques with historical and material links. Based on time delay, the Mackey–Glass model is explored in two instances. To offer a range, the relevance of the parameter impacting stability (bifurcation) is recorded.
Shuai Zhang, Yaya Wang, Hongyin Geng, Wei Gao, Esin Ilhan, Haci Mehmet Baskonus   +5 more
wiley   +1 more source

Transient Instability and Patterns of Reactivity in Diffusive-Chemotaxis Soil Carbon Dynamics. [PDF]

Bull Math Biol
Diele F, Krause AL, Lacitignola D, Marangi C, Monti A, Villar-Sepúlveda E.   +5 more
europepmc   +1 more source

High Relative Accuracy Computations With Covariance Matrices of Order Statistics

Mathematical Methods in the Applied Sciences, EarlyView.
ABSTRACT In many statistical applications, numerical computations with covariance matrices need to be performed. The error made when performing such numerical computations increases with the condition number of the covariance matrix, which is related to the number of variables and the strength of the correlation between the variables. In a recent work,
Juan Baz, Pedro Alonso, Juan Manuel Peña, Raúl Pérez‐Fernández   +3 more
wiley   +1 more source