A non-parametric and scale-independent method for cluster analysis II: the multivariate case

A general method is described for detecting and analysing galaxy systems. The multivariate geometrical structure of the sample is studied by using an extension of the method which we introduced in a previous paper. The method is based on an estimate of the probability density underlying a data sample. The density is estimated by using an iterative and adaptive kernel estimator. The used kernels have spherical symmetry, however we describe a method in order to estimate the locally optimal shape of the kernels. We use the results of the geometrical structure analysis in order to study the effects that is has on the cluster parameter estimate. This suggests a possible way to distinguish between structure and substructure within a sample. The method is tested by using simulated numerical models and applied to two galaxy samples taken from the literature. The results obtained for the Coma cluster suggest a core-halo structure formed by a large number of geometrically independent systems. A different conclusion is suggested by the results for the Cancer cluster indicating the presence of at least two independent structures both containing substructure. The dynamical consequences of the results obtained from the geometrical analysis will be described in a later paper. Further applications of the method are suggested and are currently in progress.Comment: To appear in Monthly Notices of R.A.S., 50 pages of text, latex file, aasms style, figures are available on request from the Autho

Water droplet excess free energy determined by cluster mitosis using guided molecular dynamics

Atmospheric aerosols play a vital role in affecting climate by influencing the properties and lifetimes of clouds and precipitation. Understanding the underlying microscopic mechanisms involved in the nucleation of aerosol droplets from the vapour phase is therefore of great interest. One key thermodynamic quantity in nucleation is the excess free energy of cluster formation relative to that of the saturated vapour. In our current study, the excess free energy is extracted for clusters of pure water modelled with the TIP4P/2005 intermolecular potential using a method based on nonequilibrium molecular dynamics and the Jarzynski relation. The change in free energy associated with the "mitosis" or division of a cluster of N water molecules into two N/2 sub-clusters is evaluated. This methodology is an extension of the disassembly procedure used recently to calculate the excess free energy of argon clusters [H. Y. Tang and I. J. Ford, Phys. Rev. E 91, 023308 (2015)]. Our findings are compared to the corresponding excess free energies obtained from classical nucleation theory (CNT) as well as internally consistent classical theory (ICCT). The values of the excess free energy that we obtain with the mitosis method are consistent with CNT for large cluster sizes but for the smallest clusters, the results tend towards ICCT; for intermediate sized clusters, we obtain values between the ICCT and CNT predictions. Furthermore, the curvature-dependent surface tension which can be obtained by regarding the clusters as spherical droplets of bulk density is found to be a monotonically increasing function of cluster size for the studied range. The data are compared to other values reported in the literature, agreeing qualitatively with some but disagreeing with the values determined by Joswiak et al. [J. Phys. Chem. Lett. 4, 4267 (2013)] using a biased mitosis approach; an assessment of the differences is the main motivation for our current study

Systematic quantum cluster typical medium method for the study of localization in strongly disordered electronic systems

Great progress has been made in the last several years towards understanding the properties of disordered electronic systems. In part, this is made possible by recent advances in quantum effective medium methods which enable the study of disorder and electron-electronic interactions on equal footing. They include dynamical mean field theory and the coherent potential approximation, and their cluster extension, the dynamical cluster approximation. Despite their successes, these methods do not enable the first-principles study of the strongly disordered regime, including the effects of electronic localization. The main focus of this review is the recently developed typical medium dynamical cluster approximation for disordered electronic systems. This method has been constructed to capture disorder-induced localization, and is based on a mapping of a lattice onto a quantum cluster embedded in an effective typical medium, which is determined self-consistently. Here we provide an overview of various recent applications of the typical medium dynamical cluster approximation to a variety of models and systems, including single and multi-band Anderson model, and models with local and off-diagonal disorder. We then present the application of the method to realistic systems in the framework of the density functional theory.Comment: 58 pages, 46 figure

StackInsights: Cognitive Learning for Hybrid Cloud Readiness

Hybrid cloud is an integrated cloud computing environment utilizing a mix of public cloud, private cloud, and on-premise traditional IT infrastructures. Workload awareness, defined as a detailed full range understanding of each individual workload, is essential in implementing the hybrid cloud. While it is critical to perform an accurate analysis to determine which workloads are appropriate for on-premise deployment versus which workloads can be migrated to a cloud off-premise, the assessment is mainly performed by rule or policy based approaches. In this paper, we introduce StackInsights, a novel cognitive system to automatically analyze and predict the cloud readiness of workloads for an enterprise. Our system harnesses the critical metrics across the entire stack: 1) infrastructure metrics, 2) data relevance metrics, and 3) application taxonomy, to identify workloads that have characteristics of a) low sensitivity with respect to business security, criticality and compliance, and b) low response time requirements and access patterns. Since the capture of the data relevance metrics involves an intrusive and in-depth scanning of the content of storage objects, a machine learning model is applied to perform the business relevance classification by learning from the meta level metrics harnessed across stack. In contrast to traditional methods, StackInsights significantly reduces the total time for hybrid cloud readiness assessment by orders of magnitude

The Star Cluster System in the Local Group Starburst Galaxy IC 10

We present a survey of star clusters in the halo of IC 10, a starburst galaxy in the Local Group based on Subaru R band images and NOAO Local Group Survey UBVRI images. We find five new star clusters. All these star clusters are located far from the center of IC 10, while previously known star clusters are mostly in the main body. Interestingly the distribution of these star clusters shows an asymmetrical structure elongated along the east and south-west direction. We derive UBVRI photometry of 66 star clusters including these new star clusters as well as previously known star clusters. Ages of the star clusters are estimated from the comparison of their UBVRI spectral energy distribution with the simple stellar population models. We find that the star clusters in the halo are all older than 1 Gyr, while those in the main body have various ages from very young (several Myr) to old (>1 Gyr). The young clusters (<10 Myr) are mostly located in the H{\alpha} emission regions and are concentrated on a small region at 2' in the south-east direction from the galaxy center, while the old clusters are distributed in a wider area than the disk. Intermediate-age clusters (~100 Myr) are found in two groups. One is close to the location of the young clusters and the other is at ~4' from the location of the young clusters. The latter may be related with past merger or tidal interaction.Comment: 11 pages, 12 figures, 1 table; accepted for publication in Ap