Results 111 to 120 of about 58,822 (196)

Learning Covariate Relations in Disease Progression Models Using Symbolic Neural Networks. [PDF]

CPT Pharmacometrics Syst Pharmacol
ABSTRACT Covariate modeling provides individual predictions of outcomes by disease progression models. Current methodology for mapping covariates onto model parameters is limited by predefined parametric functions which can result in inadequate covariate selection and biased predictions by the final model. Furthermore, present methodology scales poorly
Sundell J, Wahlquist Y, Kjellsson MC, Karlsson MO, Soltesz K.   +4 more
europepmc   +2 more sources

Construction of an individualized brain metabolic network in patients with advanced non-small cell lung cancer by the Kullback-Leibler divergence-based similarity method: A study based on 18F-fluorodeoxyglucose positron emission tomography. [PDF]

Front Oncol, 2023
Yu J, Hua L, Cao X, Chen Q, Zeng X, Yuan Z, Wang Y.   +6 more
europepmc   +1 more source

Predicting Learning: Understanding the Role of Executive Functions in Children's Belief Revision Using Bayesian Models

Topics in Cognitive Science, EarlyView.
Abstract Recent studies suggest that learners who are asked to predict the outcome of an event learn more than learners who are asked to evaluate it retrospectively or not at all. One possible explanation for this “prediction boost” is that it helps learners engage metacognitive reasoning skills that may not be spontaneously leveraged, especially for ...
Joseph A. Colantonio, Igor Bascandziev, Maria Theobald, Garvin Brod, Elizabeth Bonawitz   +4 more
wiley   +1 more source

Future Climate Projections of Hazardous Convective Weather Using an Ensemble of Environment‐Informed, Convection‐Permitting Dynamical Downscaling Simulations

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 7, 16 April 2026.
Abstract Convection‐permitting dynamical downscaling (CPDD) allows for an explicit representation of the convective storms that generate tornadoes, hail, severe thunderstorm winds, and locally heavy precipitation. Possible changes in such hazardous convective weather (HCW) due to human‐induced climate change are therefore projected with higher ...
Songning Wang, Robert J. Trapp, John Allen, Deepak Gopalakrishnan, Eric Robinson   +4 more
wiley   +1 more source

A Dynamic Model for Extreme Hourly Precipitation

Environmetrics, Volume 37, Issue 3, April 2026.
ABSTRACT Despite the scarcity of comprehensive studies at a global scale, many regional analyses report increases in extreme hourly precipitation values. The growing interest in assessing trends in extreme hourly precipitation has outpaced the development of new statistical tools tailored to their features. Typical analyses employ Extreme Value Theory (
Debbie J. Dupuis, Carlotta Pacifici, Luca Trapin   +2 more
wiley   +1 more source

Scour‐Conditioned Seismic Fragility Analysis of Monopile‐Supported Offshore Wind Turbines

Earthquake Engineering &Structural Dynamics, Volume 55, Issue 4, Page 947-967, 10 April 2026.
ABSTRACT Monopile‐supported offshore wind turbines (MS‐OWTs) are increasingly deployed in seismic coastal regions, where they face compound risks from earthquake loading and seabed scour. While past studies have addressed these hazards separately, seismic fragility under evolving scour conditions remains insufficiently understood. This study introduces
Francisco Pinto, Rodrigo Astroza, Felipe Vicencio, Raffaele De Risi   +3 more
wiley   +1 more source

Anomaly Detection for Structural and Functional Connectivity in Glioma Patients

NMR in Biomedicine, Volume 39, Issue 4, April 2026.
Structural connectivity (SC) and functional connectivity (FC) provide crucial insights into glioma‐brain interactions. This study uses variational autoencoder to integrate FC and SC, detect anomalies, and investigate connectivity impairments in networks proximal and distal to the tumor.
Maria Colpo, Ryan Pollitt, Alexander Leemans, Diego Cecchin, Maurizio Corbetta, Alessandra Bertoldo, Alberto De Luca   +6 more
wiley   +1 more source

Viewpoint Selection for 3D-Games with f-Divergences

Entropy
In this paper, we present a novel approach for the optimal camera selection in video games. The new approach explores the use of information theoretic metrics f-divergences, to measure the correlation between the objects as viewed in camera frustum and ...
Micaela Y. Martin, Mateu Sbert, Miguel Chover   +2 more
doaj   +1 more source

Supervised Swin Transformer‐Based Predictive Lithological Mapping and Uncertainty Quantification Using Aeromagnetic and Gravity Data

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Lithological mapping is essential for the exploration of critical minerals supporting energy transition and national defense. Although recent advancements have incorporated multi‐source data sets and leveraged machine learning and deep learning (DL) methods, lithological mapping continues to face significant challenges, such as data imbalance,
Liang Ding, Gilles Bellefleur, Olivier Boulanger, Phuong Vo   +3 more
wiley   +1 more source

Application of Variational Autoencoder‐Based Clustering for Geophysical Fluid Circulations With Small Sample Size

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract The variational autoencoder (VAE), a deep generative model, can extract a good feature representation for clustering from complex data; however, the use of this algorithm in the geophysical fluid circulation has been limited. The sample size for a geophysical phenomenon is generally small because of a large dimensional size, especially for ...
Kunihiro Aoki, Hideyuki Nakano, Nariaki Hirose, Norihisa Usui, Kei Sakamoto, Takahiro Toyoda, Shogo Urakawa, Yuma Kawakami   +7 more
wiley   +1 more source