Climbing halo merger trees with TreeFrog

We present TreeFrog, a massively parallel halo merger tree builder that is capable comparing different halo catalogues and producing halo merger trees. The code is written in c++11, use the MPI and OpenMP API’s for parallelisation, and includes python tools to read/manipulate the data products produced. The code correlates binding energy sorted particle ID lists between halo catalogues, determining optimal descendant/progenitor matches using multiple snapshots, a merit function that maximises the number of shared particles using pseudo-radial moments, and a scheme for correcting halo merger tree pathologies. Focusing on VELOCIraptor catalogues for this work, we demonstrate how searching multiple snapshots spanning a dynamical time significantly reduces the number of stranded halos, those lacking a descendant or a progenitor, critically correcting poorly resolved halos. We present a new merit function that improves the distinction between primary and secondary progenitors, reducing tree pathologies. We find FOF accretion rates and merger rates show similar mass ratio dependence. The model merger rates from Poole, et al. [2017, 472, 3659] agree with the measured net growth of halos through mergers.

A collection of forcefield precursors for metal–organic frameworks

Atom-in-material (AIM) partial charges, dipoles and quadrupoles, dispersion coefficients (C6, C8, C10), polarizabilities, electron cloud parameters, radial moments, and atom types were extracted from quantum chemistry calculations for >3000 MOFs.

Radial Moments for Image Retrieval

Images have always been considered an effective medium for presenting visual data in numerous applications ranging from industry to academia. Consequently, managing and indexing of images become essential in order to retrieve relevant images effectively and efficiently. Therefore, the proposed chapter aims to elaborate one of the advanced concepts of image processing, i.e., Content Based Image Retrieval (CBIR) and image feature extraction using advanced methods known as radial moments. In this chapter, various radial moments are discussed with their properties. Besides, performance measures and various similarity measures are elaborated in depth. The performance of radial moments is evaluated through an extensive set of experiments on benchmark databases such as Kimia-99, MPEG-7, COIL-100, etc.

Radial Moments for Image Retrieval

Images have always been considered an effective medium for presenting visual data in numerous applications ranging from industry to academia. Consequently, managing and indexing of images become essential in order to retrieve relevant images effectively and efficiently. Therefore, the proposed chapter aims to elaborate one of the advanced concepts of image processing, i.e., Content Based Image Retrieval (CBIR) and image feature extraction using advanced methods known as radial moments. In this chapter, various radial moments are discussed with their properties. Besides, performance measures and various similarity measures are elaborated in depth. The performance of radial moments is evaluated through an extensive set of experiments on benchmark databases such as Kimia-99, MPEG-7, COIL-100, etc.

Magnitude and Phase of Discriminative Orthogonal Radial Moments for Face Recognition

It is well known that the careful selection of a set of features, with higher discrimination competence, may increase recognition performance. In general, the magnitude coefficients of some selected orders of ZMs and PZMs have been used as invariant image features. The authors have used a statistical method to estimate the discrimination strength of all the coefficients of ZMs and PZMs. For classification, only the coefficients with estimated higher discrimination strength are selected and are used in the feature vector. The performance of these selected Discriminative ZMs (DZMs) and Discriminative PZMs (DPZMs) features are compared to that of their corresponding conventional approaches on YALE, ORL, and FERET databases against illumination, expression, scale, and pose variations. In this chapter, an extension to these DZMs and DPZMs is presented by exploring the use of phase information along with the magnitude coefficients of these approaches. As the phase coefficients are computed in parallel to the magnitude, no additional time is spent on their computation. Further, DZMs and DPZMs are also combined with PCA and FLD. It is observed from the exhaustive experimentation that with the inclusion of phase features the recognition rate is improved by 2-8%, at reduced dimensions and with less computational complexity, than that of using the successive ZMs and PZMs features.