Results 101 to 110 of about 165,608 (287)
Advanced Science, EarlyView.We introduce AutomataGPT, a generative pretrained transformer (GPT) trained on synthetic spatiotemporal data from 2D cellular automata to learn symbolic rules. Demonstrating strong performance on both forward and inverse tasks, AutomataGPT establishes a scalable, domain‐agnostic framework for interpretable modeling, paving the way for future ...
Jaime A. Berkovich +2 more
wiley +1 more source
Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package
Journal of Statistical Software, 2017 In multivariate nonparametric analysis curse of dimensionality forces one to use large smoothing parameters. This leads to a biased smoother. Instead of focusing on optimally selecting the smoothing parameter, we fix it to some reasonably large value to ...
Pierre-André Cornillon +2 more
doaj +1 more source
Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain
Advanced Science, EarlyView.A new approach uses simple neural networks to create digital twins of brain activity, capturing how different patterns unfold over time. The method generates and recovers key dynamics even from noisy data. When applied to fMRI, it predicts brain signals and reveals distinctive activity patterns across regions and individuals, opening possibilities for ...
Gabriele Di Antonio +3 more
wiley +1 more source
Kernel Based Goodness-of-Fit Tests for Copulas with Fixed Smoothing Parameters [PDF]
We study a test statistic on the integrated squared difference between a kernel estimator of the copula density and a kernel smoothed estimator of the parametric copula density.
Olivier Scaillet
core
Advanced Science, EarlyView.Skeleton‐oriented object segmentation (SKOOTS) introduces a new strategy for 3D mitochondrial instance segmentation by predicting explicit skeletons rather than relying on boundary cues. This approach enables robust analysis of densely packed organelles in large FIB‐SEM datasets.
Christopher J. Buswinka +3 more
wiley +1 more source
Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model
MathematicsIn this paper, we propose a smoothing estimation method for censored quantile regression models. The method associates the convolutional smoothing estimation with the loss function, which is quadratically derivable and globally convex by using a non ...
Mingquan Wang +5 more
doaj +1 more source
Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI
Advanced Science, EarlyView.ABSTRACT 2D cine phase contrast (CPC) MRI provides quantitative information on blood velocity and flow within the human vasculature. However, data acquisition is time‐consuming, motivating the reconstruction of the velocity field from undersampled measurements to reduce scan times. In this work, neural fields are proposed as a continuous spatiotemporal
Pablo Arratia +7 more
wiley +1 more source
Advanced Science, EarlyView.A zebrafish model carrying an identical human RHO S334X allele reveals two independent genetic layers shaping retinitis pigmentosa (RP) severity: a protective 3‐bp cis‐regulatory insertion that attenuates transgene expression, and a dominant trans‐acting modifier that restores a severe phenotype.
Cong Cui +9 more
wiley +1 more source
Integrating Lipschitz Extensions and Probabilistic Modelling for Metric Space Classification
MathematicsLipschitz-based classification provides a flexible framework for general metric spaces, naturally adapting to complex data structures without assuming linearity.
Roger Arnau +2 more
doaj +1 more source
The Structurally Smoothed Graphlet Kernel
, 2014 A commonly used paradigm for representing graphs is to use a vector that contains normalized frequencies of occurrence of certain motifs or sub-graphs. This vector representation can be used in a variety of applications, such as, for computing similarity between graphs. The graphlet kernel of Shervashidze et al. [32] uses induced sub-graphs of k nodes (
Yanardag, Pinar, Vishwanathan, S. V. N.
openaire +2 more sources