相关论文: Cluster membership analysis with supervised learni…
The open clusters fundamental physical parameters are important tools to understand the formation and evolution of the Galactic disk and as grounding tests for star formation and evolution models. However only a small fraction of the known…
The provision of social care applications is crucial for elderly people to improve their quality of life and enables operators to provide early interventions. Accurate predictions of user dropouts in healthy ageing applications are…
Bayesian model-based clustering is a widely applied procedure for discovering groups of related observations in a dataset. These approaches use Bayesian mixture models, estimated with MCMC, which provide posterior samples of the model…
We use 3-D K Means clustering to characterize galaxy substructure in the Abell 2146 cluster of galaxies (z = 0.2343). This method objectively characterizes the cluster's substructure using projected position and velocity data for 67…
Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large scale datasets. In this…
Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism assumption can result in suboptimal performance. We…
Motion segmentation is a fundamental problem in computer vision and is crucial in various applications such as robotics, autonomous driving and action recognition. Recently, spectral clustering based methods have shown impressive results on…
We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning $0 \leq z \leq 2$ from \textsc{The Three Hundred} project, a suite of…
Many open clusters listed in modern catalogues were initially reported by visual astronomers as apparent overdensities of bright stars. As observational techniques and analysis methods continue to improve, some of them have been shown to be…
We consider clustering in group decision making where the opinions are given by pairwise comparison matrices. In particular, the k-medoids model is suggested to classify the matrices since it has a linear programming problem formulation…
The analysis of the spatial distribution and kinematics of galaxies in clusters allows one to determine the cluster internal dynamics. In this paper, I review the state of the art of this topic. In particular, I summarize what we have…
Conventionally, CMD analyses of nearby star clusters are based on observations in 2 passbands. They are plagued by considerable degeneracies between age, metallicity, distance (and extinction) that can largely be resolved by including…
Star clusters are crucial for understanding how stars evolve. Their colour-magnitude diagrams show the effects of stellar evolution of approximately coeval objects with the same chemical composition. Furthermore, the determination of their…
The distribution of mass in the halos of galaxies and galaxy clusters has been probed observationally, theoretically, and in numerical simulations. Yet there is still confusion about which of several suggested parameterized models is the…
This paper describes a practical approach of using supervised machine learning (ML) models to assist safety investigators to classify aviation occurrences into either incident or serious incident categories. Our implementation currently…
We have obtained HST-NICMOS observations of five of M31's most metal rich globular clusters. These data allow photometry of individual stars in the clusters and their surrounding fields. However, to achieve our goals -- obtain accurate…
Breast cancer (BC) remains a significant global health challenge, with personalized treatment selection complicated by the disease's molecular and clinical heterogeneity. BC treatment decisions rely on various patient-specific clinical…
The efficient classification of different types of supernova is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin…
Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and…
Open clusters have long been used as tracers of Galactic structure. However, without a selection function to describe the completeness of the cluster census, it is difficult to quantitatively interpret their distribution. We create a method…