Correcting nuisance variation using Wasserstein distance [PDF]
PeerJ, 2020 Profiling cellular phenotypes from microscopic imaging can provide meaningful biological information resulting from various factors affecting the cells.
Gil Tabak +4 more
doaj +6 more sources
Alignment of density maps in Wasserstein distance. [PDF]
Biol Imaging, 2023 In this article, we propose an algorithm for aligning three-dimensional objects when represented as density maps, motivated by applications in cryogenic electron microscopy.
Singer A, Yang R.
europepmc +7 more sources
Wasserstein Distance-Based Deep Leakage from Gradients [PDF]
Entropy, 2023 Federated learning protects the privacy information in the data set by sharing the average gradient. However, “Deep Leakage from Gradient” (DLG) algorithm as a gradient-based feature reconstruction attack can recover privacy training data using gradients
Zifan Wang +3 more
doaj +2 more sources
Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance. [PDF]
BMC Biol, 2023 Background Enormous clinical and biomedical researches have demonstrated that microbes are crucial to human health. Identifying associations between microbes and diseases can not only reveal potential disease mechanisms, but also facilitate early ...
Zhu H, Hao H, Yu L.
europepmc +2 more sources
Dissimilarity measure of local structure in inorganic crystals using Wasserstein distance to search for novel phosphors [PDF]
Science and Technology of Advanced Materials, 2021 To efficiently search for novel phosphors, we propose a dissimilarity measure of local structure using the Wasserstein distance. This simple and versatile method provides the quantitative dissimilarity of a local structure around a center ion.
Shota Takemura +5 more
doaj +2 more sources
Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance [PDF]
NeuroImage, 2023 Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that ...
Moo K. Chung +9 more
doaj +2 more sources
Matricial Wasserstein-1 Distance [PDF]
IEEE Control Systems Letters, 2017 In this note, we propose an extension of the Wasserstein 1-metric ($W_1$) for matrix probability densities, matrix-valued density measures, and an unbalanced interpretation of mass transport. The key is using duality theory, in particular, a "dual of the
Chen, Yongxin +3 more
core +5 more sources
Quantum Wasserstein distance based on an optimization over separable states [PDF]
Quantum, 2023 We define the quantum Wasserstein distance such that the optimization of the coupling is carried out over bipartite separable states rather than bipartite quantum states in general, and examine its properties. Surprisingly, we find that the self-distance
Géza Tóth, József Pitrik
doaj +3 more sources
Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss. [PDF]
IEEE Trans Med Imaging, 2018 The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient.
Yang Q +9 more
europepmc +3 more sources
Max-Sliced Wasserstein Distance and its use for GANs [PDF]
Computer Vision and Pattern Recognition, 2019 Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning.
Deshpande, Ishan +8 more
core +2 more sources