Related papers: Empirical Globular Cluster Ridgeline Construction …
Grid visualizations are widely used in many applications to visually explain a set of data and their proximity relationships. However, existing layout methods face difficulties when dealing with the inherent cluster structures within the…
We introduce a new type of diagrams and prove the existence of a particular one, the "central tuned diagram", with some optimal features, for finitely generated modules of certain categories. This is achieved by getting to the idea of "the…
This work discusses the model reduction problem for large-scale multi-symplectic PDEs with cubic invariants. For this, we present a linearly implicit global energy-preserving method to construct reduced-order models. This allows to…
We place constraints on the formation redshifts for blue globular clusters (BGCs), independent of the details of hydrodynamics and population III star formation. The observed radial distribution of BGCs in the Milky Way Galaxy suggests that…
A computational approach via implementation of the Principle Component Analysis (PCA) and Gaussian Mixture (GM) clustering methods from Machine Learning (ML) algorithms to identify domain structures of supercooled liquids is developed. Raw…
We study the geometry and topology of the large-scale structure traced by galaxy clusters in numerical simulations of a box of side 320 $h^{-1}$ Mpc, and compare them with available data on real clusters. The simulations we use are…
Clustering is an unsupervised learning problem that aims to partition unlabelled data points into groups with similar features. Traditional clustering algorithms provide limited insight into the groups they find as their main focus is…
Our numerical simulations first demonstrate that the pressure of ISM in a major merger becomes so high ($>$ $10^5$ $\rm k_{\rm B}$ K $\rm cm^{-3}$) that GMCs in the merger can collapse to form globular clusters (GCs) within a few Myr. The…
The classical Gaussian mixture model assumes homogeneity within clusters, an assumption that often fails in real-world data where observations naturally exhibit varying scales or intensities. To address this, we introduce the…
We present the first results of a project aimed at following the formation and long-term dynamical evolution of star clusters within the potential of a host galaxy. Here we focus on a model evolved within a simplified potential representing…
We show the existence of generalized clusters of a finite or even infinite number of sets, with minimal total perimeter and given total masses, in metric measure spaces homogeneous with respect to a group acting by measure preserving…
There is a growing interest in characterizing circular data found in biological systems. Such data are wide ranging and varied, from signal phase in neural recordings to nucleotide sequences in round genomes. Traditional clustering…
A new method that accurately describes strongly correlated states and captures dynamical correlation is presented. It is derived as a modification of coupled-cluster theory with single and double excitations (CCSD) through consideration of…
We present a high-resolution simulation of globular cluster formation in a galaxy merger. For the first time in such a simulation, individual star clusters are directly identified and followed on their orbits. We quantitatively compare star…
Modularity clustering is an essential tool to understand complicated graphs. However, existing methods are not applicable to massive graphs due to two serious weaknesses. (1) It is difficult to fully reproduce ground-truth clusters due to…
In this survey we describe a recently-developed technique for bounding the number (and controlling the typical structure) of finite objects with forbidden substructures. This technique exploits a subtle clustering phenomenon exhibited by…
Evidence that the multiple populations (MPs) are common properties of globular clusters (GCs) is accumulated over the past decades from clusters in the Milky Way and in its satellites. This finding has revived GC research, and suggested…
Reformulating computer vision problems over Riemannian manifolds has demonstrated superior performance in various computer vision applications. This is because visual data often forms a special structure lying on a lower dimensional space…
We test the hypothesis that metal-poor globular clusters form within disk galaxies at redshifts z>3. Numerical simulations demonstrate that giant gas clouds, which are cold and dense enough to produce massive star clusters, assemble…
Scientists in many fields have the common and basic need of dimensionality reduction: visualizing the underlying structure of the massive multivariate data in a low-dimensional space. However, many dimensionality reduction methods confront…