Results 91 to 100 of about 33,205 (174)

A View on Optimal Transport from Noncommutative Geometry

Symmetry, Integrability and Geometry: Methods and Applications, 2010
We discuss the relation between the Wasserstein distance of order 1 between probability distributions on a metric space, arising in the study of Monge-Kantorovich transport problem, and the spectral distance of noncommutative geometry.
Francesco D'Andrea, Pierre Martinetti
doaj   +1 more source

Advancing Marine Bioacoustics With Deep Generative Models: A Hybrid Augmentation Strategy for Southern Resident Killer Whale Detection

Marine Mammal Science, Volume 42, Issue 3, July 2026.
ABSTRACT Automated detection and classification of marine mammal vocalizations is critical for conservation and management efforts but is hindered by limited annotated datasets and the acoustic complexity of real‐world marine environments. Data augmentation has proven to be an effective strategy to address this limitation by increasing dataset ...
Bruno Padovese, Fabio Frazao, Michael Dowd, Ruth Joy   +3 more
wiley   +1 more source

A Spectral Matching Algorithm Based on the Wasserstein Metric

Journal of Chemometrics, Volume 40, Issue 6, June 2026.
ABSTRACT Through visual inspection, scientists can easily judge the similarity of pairs of spectra from different sources, whether they are experimental measurements, spectra libraries, or spectra calculations. Spectral similarity is recognized when the peak patterns appear similar, even if the peak positions are shifted or the peak amplitudes are ...
Klaus Neymeyr, Baoxin Zhang, Christoph Kubis, Mattis Brinker, Lukas Prestin, Tomass Andersons, Mathias Sawall, Jan Hellwig   +7 more
wiley   +1 more source

Entropy–Mean–Upper Partial Deviation–Absolute Deviation Portfolio Problem and Its Wasserstein Distributionally Robust Counterpart

IEEE Access
This paper proposes an Entropy–Mean–Upper partial deviation–Absolute Deviation (EMUAD) portfolio problem, introducing entropy to reduce investment risk and enhance portfolio diversification while simultaneously considering metrics ...
Haonan Wang, Mingyang Fan, Bowen Liu
doaj   +1 more source

Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance. [PDF]

BMC Biol, 2023
Zhu H, Hao H, Yu L.
europepmc   +1 more source

Covariance Structure Modeling of Engineering Demand Parameters in Cloud‐Based Seismic Analysis

Earthquake Engineering &Structural Dynamics, Volume 55, Issue 7, Page 1533-1551, June 2026.
ABSTRACT Probabilistic seismic demand modeling aims to estimate structural demand as a function of ground motion intensity—a critical stage in seismic risk assessment. Although many models exist to describe the structural demand, few consider the covariance among engineering demand parameters, potentially overlooking a key factor in improving the ...
Archie Rudman, Enrico Tubaldi, Roberto Gentile, John Douglas   +3 more
wiley   +1 more source

Parameter estimation from aggregate observations: a Wasserstein distance-based sequential Monte Carlo sampler. [PDF]

R Soc Open Sci, 2023
Cheng C, Wen L, Li J.
europepmc   +1 more source

Input Layer Regularization and Automated Regularization Hyperparameter Tuning for Myelin Water Estimation Using Deep Learning

NMR in Biomedicine, Volume 39, Issue 6, June 2026.
We propose a novel deep learning algorithm for predicting the myelin water fraction from multiple gradient‐echo or spin‐echo pulse sequences arising in magnetic resonance relaxometry (MRR) measurements of the human brain. Our method incorporates both regularized nonlinear least squares and pure deep learning through a concatenation paradigm known as ...
Mirage Modi, Shashank Sule, Jonathan Palumbo, Michael Rozowski, Griffin S. Hampton, Mustapha Bouhrara, Wojciech Czaja, Richard G. Spencer   +7 more
wiley   +1 more source

Skorohod Representation Theorem Via Disintegrations [PDF]

Let (µn : n >= 0) be Borel probabilities on a metric space S such that µn -> µ0 weakly. Say that Skorohod representation holds if, on some probability space, there are S-valued random variables Xn satisfying Xn - µn for all n and Xn -> X0 in probability.
Luca Pratelli, Patrizia Berti, Pietro Rigo   +2 more
core  

Distribution‐Guided Ensemble Postprocessing for S2S Precipitation Forecasts: A Seamless Pathway Using Deep Generative Models

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract Atmosphere‐ocean‐land coupled forecasting systems, despite their comprehensiveness, face substantial challenges in the “predictability desert” at subseasonal to seasonal (S2S) timescales, particularly for precipitation—a variable crucial for socioeconomic activities yet of stunning spatiotemporal variance. Post‐processing methods developed for
Wen Shi, Baoxiang Pan, Jianbin Huang, Tingfeng Dou, Jie Feng, Huihui Yuan, Anran Wang, Xin Xia, Lei Huang, Yong Luo   +9 more
wiley   +1 more source