arXiv subjects

Natalia Soboleva

Publications and source records attributed to Natalia Soboleva.

4 recordsLinked to original sources

arXiv2025

New Radio Data on Sources of the Big Trio Program for Searching for Distant Radio Galaxies

Radio sources with steep and ultra-steep spectra, identified through a series of surveys of the Cold experiment using the RATAN-600 radio telescope, formed the basis of the Big Trio program aimed at discovering distant radio galaxies. With the advent of new radio, optical, and infrared sky surveys, it became possible to carry out additional studies of the 113 radio sources observed within this program using the 6-meter BTA telescope. Morphological features indicative of the evolutionary state of radio sources revealed that 9 objects exhibit signs of restarted activity, 12 can be classified as young, and 2 as dying. Based on their WAT/NAT and WRS morphologies, 24 sources are likely located within galaxy groups or clusters, or show evidence of jet reorientation. Additionally, four objects in the sample represent pairs of radio sources whose host galaxies are separated by only a few tens of kiloparsecs. Continuum spectra were constructed, and variability indices were calculated. A high proportion of sources with a variability index ($V>3$) is attributed to significant differences in angular resolution between the TXS and VCSS surveys, as well as to underestimated flux densities for certain double-lobed sources. A comparison of spectral indices derived from older and newer data indicates a decrease in the number of steep-spectrum sources in the sample. This reduction is most likely due to improved characterization of the low-frequency portion of the continuum spectra following the incorporation of GLEAM data. However, for some radio sources, the observed spectral changes may reflect internal evolution, that observed in a shift of the spectral peak or a roll-off in the radio spectrum towards lower frequencies, as evidenced by flux density measurements from various catalogues covering a period of up to forty years.

astro-ph.CO↗

arXiv2023

PRS: Sharp Feature Priors for Resolution-Free Surface Remeshing

Surface reconstruction with preservation of geometric features is a challenging computer vision task. Despite significant progress in implicit shape reconstruction, state-of-the-art mesh extraction methods often produce aliased, perceptually distorted surfaces and lack scalability to high-resolution 3D shapes. We present a data-driven approach for automatic feature detection and remeshing that requires only a coarse, aliased mesh as input and scales to arbitrary resolution reconstructions. We define and learn a collection of surface-based fields to (1) capture sharp geometric features in the shape with an implicit vertexwise model and (2) approximate improvements in normals alignment obtained by applying edge-flips with an edgewise model. To support scaling to arbitrary complexity shapes, we learn our fields using local triangulated patches, fusing estimates on complete surface meshes. Our feature remeshing algorithm integrates the learned fields as sharp feature priors and optimizes vertex placement and mesh connectivity for maximum expected surface improvement. On a challenging collection of high-resolution shape reconstructions in the ABC dataset, our algorithm improves over state-of-the-art by 26% normals F-score and 42% perceptual $\text{RMSE}_{\text{v}}$.

cs.CV↗

arXiv2019

GAN Path Finder: Preliminary results

2D path planning in static environment is a well-known problem and one of the common ways to solve it is to 1) represent the environment as a grid and 2) perform a heuristic search for a path on it. At the same time 2D grid resembles much a digital image, thus an appealing idea comes to being -- to treat the problem as an image generation task and to solve it utilizing the recent advances in deep learning. In this work we make an attempt to apply a generative neural network as a path finder and report preliminary results, convincing enough to claim that this direction of research is worth further exploration.

cs.LG↗

arXiv2018

eLIAN: Enhanced Algorithm for Angle-constrained Path Finding

Problem of finding 2D paths of special shape, e.g. paths comprised of line segments having the property that the angle between any two consecutive segments does not exceed the predefined threshold, is considered in the paper. This problem is harder to solve than the one when shortest paths of any shape are sought, since the planer's search space is substantially bigger as multiple search nodes corresponding to the same location need to be considered. One way to reduce the search effort is to fix the length of the path's segment and to prune the nodes that violate the imposed constraint. This leads to incompleteness and to the sensitivity of the 's performance to chosen parameter value. In this work we introduce a novel technique that reduces this sensitivity by automatically adjusting the length of the path's segment on-the-fly, e.g. during the search. Embedding this technique into the known grid-based angle-constrained path finding algorithm - LIAN, leads to notable increase of the planner's effectiveness, e.g. success rate, while keeping efficiency, e.g. runtime, overhead at reasonable level. Experimental evaluation shows that LIAN with the suggested enhancements, dubbed eLIAN, solves up to 20\% of tasks more compared to the predecessor. Meanwhile, the solution quality of eLIAN is nearly the same as the one of LIAN.

cs.AI↗