Biogeochemical accumulation of trace elements in soils and plants of the Russian Far East

The accumulation of total mercury and other elements in soils and plants growing on them (legume and valerian families) selected in the Khabarovsk and Primorsky Territories, Buryatia, and the Amur Region was studied. The possibility of determining mercury in natural objects along with the natural atomic absorption method is also shown by X-ray fluorescence spectroscopy simultaneously with other trace elements. Analysis of the data obtained indicates that the main sources of trace elements at the test sites are, apparently, sources of natural origin – soils, underlying rocks, underground and surface waters, etc

Variational Level-Set Detection of Local Isosurfaces from Unstructured Point-based Volume Data

A standard approach for visualizing scalar volume data is the extraction of isosurfaces. The most efficient methods for surface extraction operate on regular grids. When data is given on unstructured point-based samples, regularization can be applied but may introduce interpolation errors. We propose a method for smooth isosurface visualization that operates directly on unstructured point-based volume data avoiding any resampling. We derive a variational formulation for smooth local isosurface extraction using an implicit surface representation in form of a level-set approach, deploying Moving Least Squares (MLS) approximation, and operating on a kd-tree. The locality of our approach has two aspects: first, our algorithm extracts only those components of the isosurface, which intersect a subdomain of interest; second, the action of the main term in the governing equation is concentrated near the current isosurface position. Both aspects reduce the computation times per level-set iteration. As for most level-set methods a reinitialization procedure is needed, but we also consider a modified algorithm where this step is eliminated. The final isosurface is extracted in form of a point cloud representation. We present a novel point completion scheme that allows us to handle highly adaptive point sample distributions. Subsequently, splat-based or mere (shaded) point rendering is applied. We apply our method to several synthetic and real-world data sets to demonstrate its validity and efficiency

Extreme acoustic anisotropy in crystals visualized by diffraction tensor

Acoustic wave propagation in single crystals, metamaterials and composite structures is a basic mechanism in acoustic, acousto-electronic and acousto-optic devices. Acoustic anisotropy of crystals provides a variety of device performances and application fields, but its role in pre-estimation of achievable device characteristics and location of crystal orientations with desired properties is often underestimated. A geometrical image of acoustic anisotropy can be an important tool in design of devices based on wave propagation in single crystals or combinations of anisotropic materials. We propose a fast and robust method for survey and visualization of acoustic anisotropy based on calculation of the eigenvalues of bulk acoustic wave (BAW) diffraction tensor (curvature of the slowness surface). The stereographic projection of these eigenvalues clearly reveals singular directions of BAW propagation (acoustic axes) in anisotropic media and areas of fast or slow variation of wave velocities. The method is illustrated by application to three crystals of different symmetry used in different types of acoustic devices: paratellurite, lithium niobate, and potassium gadolinium tungstate. The specific features of acoustic anisotropy are discussed for each crystal in terms of their potential application in devices. In addition, we demonstrate that visualization of acoustic anisotropy of lithium niobate helps to find orientations supporting propagation of high-velocity surface acoustic waves.Comment: 12 pages, preprint submitted to EPJ Plu

Star-Shaped Denoising Diffusion Probabilistic Models

Methods based on Denoising Diffusion Probabilistic Models (DDPM) became a ubiquitous tool in generative modeling. However, they are mostly limited to Gaussian and discrete diffusion processes. We propose Star-Shaped Denoising Diffusion Probabilistic Models (SS-DDPM), a model with a non-Markovian diffusion-like noising process. In the case of Gaussian distributions, this model is equivalent to Markovian DDPMs. However, it can be defined and applied with arbitrary noising distributions, and admits efficient training and sampling algorithms for a wide range of distributions that lie in the exponential family. We provide a simple recipe for designing diffusion-like models with distributions like Beta, von Mises--Fisher, Dirichlet, Wishart and others, which can be especially useful when data lies on a constrained manifold such as the unit sphere, the space of positive semi-definite matrices, the probabilistic simplex, etc. We evaluate the model in different settings and find it competitive even on image data, where Beta SS-DDPM achieves results comparable to a Gaussian DDPM