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Generative models rely on the idea that data can be represented in terms of latent variables which are uncorrelated by definition. Lack of correlation among the latent variable support is important because it suggests that the latent-space manifold is simpler to understand and manipulate than the real-space representation. Many types of generative model are used in deep learning,e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs). Based on the idea that the latent space behaves like a vector space Radford et al. (2015), we ask whether we can expand the latent space representation of our data elements in terms of an orthonormal basis set. Here we propose a method to build a set of linearly independent vectors in the latent space of a trained GAN, which we call quasi-eigenvectors. These quasi-eigenvectors have two key properties: i) They span the latent space, ii) A set of these quasi-eigenvectors map to each of the labeled features one-to-one. We show that in the case of the MNIST image data set, while the number of dimensions in latent space is large by design, 98% of the data in real space map to a sub-domain of latent space of dimensionality equal to the number of labels. We then show how the quasi-eigenvectors can be used for Latent Spectral Decomposition (LSD). We apply LSD to denoise MNIST images. Finally, using the quasi-eigenvectors, we construct rotation matrices in latent space which map to feature transformations in real space. Overall, from quasi-eigenvectors we gain insight regarding the latent space topology. 

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019 ; Lee et al., 2020 ). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution. 

We present the first measurement of cosmic-ray fluxes of ${}^6\mathrm{Li}$and ${}^7\mathrm{Li}$isotopes in the rigidity range from 1.9 to 25 GV. The measurements are based on $9.7\times {10}^5$ ${}^6\mathrm{Li}$and $1.04\times {10}^6$ ${}^7\mathrm{Li}$nuclei collected by the Alpha Magnetic Spectrometer on the International Space Station from May 2011 to October 2023. We observe that over the entire rigidity range the ${}^6\mathrm{Li}$and ${}^7\mathrm{Li}$fluxes exhibit nearly identical time variations and, above $\sim 4\mathrm{GV}$, the time variations of ${}^6\mathrm{Li}$, ${}^7\mathrm{Li}$, He, Be, B, C, N, and O fluxes are identical. Above $\sim 7\mathrm{GV}$, we find an identical rigidity dependence of the ${}^6\mathrm{Li}$and ${}^7\mathrm{Li}$fluxes. This shows that they are both produced by collisions of heavier cosmic-ray nuclei with the interstellar medium and, in particular, excludes the existence of a sizable primary component in the ${}^7\mathrm{Li}$flux. Published by the American Physical Society2025 

We report the properties of precision time structures of cosmic nuclei He, Li, Be, B, C, N, and O fluxes over an 11-year solar cycle from May 2011 to November 2022 in the rigidity range from 1.92 to 60.3 GV. The nuclei fluxes show similar but not identical time variations with amplitudes decreasing with increasing rigidity. In particular, below 3.64 GV the Li, Be, and B fluxes, and below 2.15 GV the C, N, and O fluxes, are significantly less affected by solar modulation than the He flux. We observe that these differences in solar modulation are linearly correlated with the differences in the spectral indices of the cosmic nuclei fluxes. This shows, in a model-independent way, that solar modulation of galactic cosmic nuclei depends on their spectral shape. In addition, solar modulation differences due to nuclei velocity dependence on the mass-to-charge ratio ( $A/Z$) are not observed. Published by the American Physical Society2025 

Precision measurements by the Alpha Magnetic Spectrometer (AMS) on the International Space Station of the deuteron ( $D$) flux are presented. The measurements are based on $21\times {10}^6$ $D$nuclei in the rigidity range from 1.9 to 21 GV collected from May 2011 to April 2021. We observe that over the entire rigidity range the $D$flux exhibits nearly identical time variations with the $p$, ${}^3\mathrm{He}$, and ${}^4\mathrm{He}$fluxes. Above 4.5 GV, the $D/{}^4\mathrm{He}$flux ratio is time independent and its rigidity dependence is well described by a single power law $\propto R^{\mathrm{\Delta }}$with ${\mathrm{\Delta }}_{D/{}^4\mathrm{He}}=-0.108\pm 0.005$. This is in contrast with the ${}^3\mathrm{He}/{}^4\mathrm{He}$flux ratio for which we find ${\mathrm{\Delta }}_{{}^3\mathrm{He}/{}^4\mathrm{He}}=-0.289\pm 0.003$. Above $\sim 13\mathrm{GV}$we find a nearly identical rigidity dependence of the $D$and $p$fluxes with a $D/p$flux ratio of $0.027\pm 0.001$. These unexpected observations indicate that cosmic deuterons have a sizable primarylike component. With a method independent of cosmic ray propagation, we obtain the primary component of the $D$flux equal to $9.4\pm 0.5\%$of the ${}^4\mathrm{He}$flux and the secondary component of the $D$flux equal to $58\pm 5\%$of the ${}^3\mathrm{He}$flux. Published by the American Physical Society2024