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Recently, a class of non-truncated radially-anisotropic models (the so-called $f^{(\nu)}$-models), originally constructed in the context of violent relaxation and modeling of elliptical galaxies, has been found to possess interesting…
Classification of rocks is one of the fundamental tasks in a geological study. The process requires a human expert to examine sampled thin section images under a microscope. In this study, we propose a method that uses microscope…
We use a new contour-based map analysis technique to measure the mass and size of molecular cloud fragments continuously over a wide range of spatial scales (0.05 < r / pc < 10), i.e., from the scale of dense cores to those of entire…
Pollen grain micrograph classification has multiple applications in medicine and biology. Automatic pollen grain image classification can alleviate the problems of manual categorisation such as subjectivity and time constraints. While a…
Fine-grained visual classification (FGVC) requires distinguishing between visually similar categories through subtle, localized features - a task that remains challenging due to high intra-class variability and limited inter-class…
Observations of interstellar extinction and polarization indicate that the interstellar medium consists of aligned non-spherical dust grains which show variation in the interstellar extinction curve for wavelengths ranging from NIR to UV.…
Understanding the molecular content of galaxies is a critical problem in star formation and galactic evolution. Here we present a new method, based on a Stromgren-type analysis, to calculate the amount of HI that surrounds a molecular cloud…
We introduce a method of estimating parameters associated with a fractal random scattering medium, which utilizes the multiscale properties of the scattered field. The example of ray-density fluctuations beyond a phase screen with fractal…
In the era of large photometric surveys, the importance of automated and accurate classification is rapidly increasing. Specifically, the separation of resolved and unresolved sources in astronomical imaging is a critical initial step for a…
In this paper we present a modeling approach to bridge the atomistic with macroscopic scales in crystalline materials. The methodology combines identification and modeling of the controlling unit processes at microscopic level with the…
We present a metaheuristic conditional neural-network-based method aimed at identifying physically interesting metastable states in a potential energy surface of high rugosity. To demonstrate how this method works, we identify and analyze…
Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance. While global approaches aim at using the whole image for performing the classification,…
We present a new and simple technique for selecting extensive, complete and pure quasar samples, based on their intrinsic variability. We parametrize the single-band variability by a power-law model for the light-curve structure function,…
In soft matter systems the local displacement field can be accessed directly by video microscopy enabling one to compute local strain fields and hence the elastic moduli using a coarse-graining procedure. We study this process for a simple…
Automatic terrain recognition in Mars rover images is an important problem not just for navigation, but for scientists interested in studying rock types, and by extension, conditions of the ancient Martian paleoclimate and habitability.…
We propose a new class of models for variable clustering called Asymptotic Independent block (AI-block) models, which defines population-level clusters based on the independence of the maxima of a multivariate stationary mixing random…
We present a new SMA survey of 47 Class II sources in the Taurus-Auriga region. Our observations made 12 independent samples of flux densities over the 200-400 GHz frequency range. We tightly constrained the spectral indices of most sources…
We present a novel method for automatically detecting and characterising semi-resolved star clusters: clusters where the observational point-spread function (PSF) is smaller than the cluster's radius, but larger than the separations between…
In this study, radar signals were analyzed to classify grain surface types by using machine learning methods. Radar backscatter signals were recorded using a vector network analyzer between 18-40 GHz. A total of 5681 measurements of A scan…
By means of post-processed cosmological Hydrodynamics simulations we explore the ability of the fossil record method to recover the stellar mass, age gradients, and global/radial star formation and mass growth histories of galaxies observed…