Global volcanic rock classification of Holocene volcanoes.

This data descriptor assigns the major and minor rock names from worldwide Holocene volcanoes of the Global Volcanism Program (GVP) using the Total Alkali-Silica diagram (TAS) for the chemical classification of volcanic rocks using the Geochemistry of Rocks of the Oceans and Continents (GEOROC) database. The precompiled files of the GEOROC database provide the chemical composition of volcanic rock samples, from which we computed major and minor rocks for global Holocene volcanoes reported in GVP. The combined dataset associates each volcano with the relative abundance of each volcanic sample type (whole rock, glass, melt inclusion) and provides the five major (more than 10% abundance) and minor rock names. In total, over 138,000 GEOROC volcanic rock samples were considered, for ~1000 Holocene volcanoes. The resulting major rock compositions are in general consistent with those given in GVP. The dataset provides a global panorama of rock composition for Holocene volcanoes.

On parsimony and clustering.

This work is motivated by applications of parsimonious cladograms for the purpose of analyzing non-biological data. Parsimonious cladograms were introduced as a means to help understanding the tree of life, and are now used in fields related to biological sciences at large, e.g., to analyze viruses or to predict the structure of proteins. We revisit parsimonious cladograms through the lens of clustering and compare cladograms optimized for parsimony with dendograms obtained from single linkage hierarchical clustering. We show that despite similarities in both approaches, there exist datasets whose clustering dendogram is incompatible with parsimony optimization. Furthermore, we provide numerical examples to compare via F-scores the clustering obtained through both parsimonious cladograms and single linkage hierarchical dendograms.

Renyi entropy driven hierarchical graph clustering.

This article explores a graph clustering method that is derived from an information theoretic method that clusters points in R n relying on Renyi entropy, which involves computing the usual Euclidean distance between these points. Two view points are adopted: (1) the graph to be clustered is first embedded into R d for some dimension d so as to minimize the distortion of the embedding, then the resulting points are clustered, and (2) the graph is clustered directly, using as distance the shortest path distance for undirected graphs, and a variation of the Jaccard distance for directed graphs. In both cases, a hierarchical approach is adopted, where both the initial clustering and the agglomeration steps are computed using Renyi entropy derived evaluation functions. Numerical examples are provided to support the study, showing the consistency of both approaches (evaluated in terms of F-scores).

On Grid Quorums for Erasure Coded Data.

We consider the problem of designing grid quorum systems for maximum distance separable (MDS) erasure code based distributed storage systems. Quorums are used as a mechanism to maintain consistency in replication based storage systems, for which grid quorums have been shown to produce optimal load characteristics. This motivates the study of grid quorums in the context of erasure code based distributed storage systems. We show how grid quorums can be built for erasure coded data, investigate the load characteristics of these quorum systems, and demonstrate how sequential consistency is achieved even in the presence of storage node failures.

A split-and-transfer flow based entropic centrality.

The notion of entropic centrality measures how central a node is in terms of how uncertain the destination of a flow starting at this node is: the more uncertain the destination, the more well connected and thus central the node is deemed. This implicitly assumes that the flow is indivisible, and at every node, the flow is transferred from one edge to another. The contribution of this paper is to propose a split-and-transfer flow model for entropic centrality, where at every node, the flow can actually be arbitrarily split across choices of neighbours. We show how to map this to an equivalent transfer entropic centrality set-up for the ease of computation, and carry out three case studies (an airport network, a cross-shareholding network and a Bitcoin transactions subnetwork) to illustrate the interpretation and insights linked to this new notion of centrality.