Molecular Model of Dynamic Social Network Based on E-mail communication

In this work we consider an application of physically inspired sociodynamical model to the modelling of the evolution of email-based social network. Contrary to the standard approach of sociodynamics, which assumes expressing of system dynamics with heuristically defined simple rules, we postulate the inference of these rules from the real data and their application within a dynamic molecular model. We present how to embed the n-dimensional social space in Euclidean one. Then, inspired by the Lennard-Jones potential, we define a data-driven social potential function and apply the resultant force to a real e-mail communication network in a course of a molecular simulation, with network nodes taking on the role of interacting particles. We discuss all steps of the modelling process, from data preparation, through embedding and the molecular simulation itself, to transformation from the embedding space back to a graph structure. The conclusions, drawn from examining the resultant networks in stable, minimum-energy states, emphasize the role of the embedding process projecting the non–metric social graph into the Euclidean space, the significance of the unavoidable loss of information connected with this procedure and the resultant preservation of global rather than local properties of the initial network. We also argue applicability of our method to some classes of problems, while also signalling the areas which require further research in order to expand this applicability domain

Paragenesis of multiple platinum-group mineral populations in Shetland ophiolite chromitite: 3D X-ray tomography and in situ Os isotopes

Chromitite from the Harold’s Grave locality in the mantle section of the Shetland ophiolite complex is extremely enriched in Ru, Os and Ir, at µg/g concentrations. High-resolution X-ray computed tomography on micro-cores from these chromitites was used to determine the location, size, distribution and morphology of the platinum-group minerals (PGM). There are five generations of PGM in these chromitites. Small (average 5 µm in equivalent sphere diameter, ESD) euhedral laurites, often with Os-Ir alloys, are totally enclosed in the chromite and are likely to have formed first by direct crystallisation from the magma as the chromite crystallised. Also within the chromitite there are clusters of larger (50 µm ESD) aligned elongate crystals of Pt-, Rh-, Ir-, Os- and Ru-bearing PGM that have different orientations in different chromite crystals. These may have formed either by exsolution, or by preferential nucleation of PGMs in boundary layers around particular growing chromite grains. Thirdly there is a generation of large (100 µm ESD) composite Os-Ir-Ru-rich PGM that are all interstitial to the chromite grains and sometimes form in clusters. It is proposed that Os, Ir and Ru in this generation were concentrated in base metal sulfide droplets that were then re-dissolved into a later sulfide-undersaturated magma, leaving PGM interstitial to the chromite grains. Fourthly there is a group of almost spherical large (80 µm ESD) laurites, hosting minor Os-Ir-Ru-rich PGM that form on the edge or enclosed in chromite grains occurring in a sheet crosscutting a chromitite layer. These may be hosted in an annealed late syn- or post magmatic fracture. Finally a few of the PGM have been deformed in localised shear zones through the chromitites. The vast majority of the PGM – including small PGM enclosed within chromite, larger interstitial PGM and elongate aligned PGM – have Os isotope compositions that give Re-depletion model ages approximately equal to the age of the ophiolite at ∼492 Ma. A number of other PGM – not confined to a single textural group – fall to more or less radiogenic values, with four PGM giving anomalously unradiogenic Os corresponding to an older age of ∼1050 Ma. The 187Os/188Os isotopic ratios for PGM from Cliff and Quoys, from the same ophiolite section, are somewhat more radiogenic than those at Harold’s Grave. This may be due to a distinct mantle source history or possibly the assimilation of radiogenic crustal Os

European wildcat populations are subdivided into five main biogeographic groups: consequences of Pleistocene climate changes or recent anthropogenic fragmentation?

Extant populations of the European wildcat are fragmented across the continent, the likely consequence of recent extirpations due to habitat loss and over-hunting. However, their underlying phylogeographic history has never been reconstructed. For testing the hypothesis that the European wildcat survived the Ice Age fragmented in Mediterranean refuges, we assayed the genetic variation at 31 microsatellites in 668 presumptive European wildcats sampled in 15 European countries. Moreover, to evaluate the extent of subspecies/population divergence and identify eventual wild × domestic cat hybrids, we genotyped 26 African wildcats from Sardinia and North Africa and 294 random-bred domestic cats. Results of multivariate analyses and Bayesian clustering confirmed that the European wild and the domestic cats (plus the African wildcats) belong to two well-differentiated clusters (average Ф ST = 0.159, r st = 0.392, P > 0.001; Analysis of molecular variance [AMOVA]). We identified from c. 5% to 10% cryptic hybrids in southern and central European populations. In contrast, wild-living cats in Hungary and Scotland showed deep signatures of genetic admixture and introgression with domestic cats. The European wildcats are subdivided into five main genetic clusters (average Ф ST = 0.103, r st = 0.143, P > 0.001; AMOVA) corresponding to five biogeographic groups, respectively, distributed in the Iberian Peninsula, central Europe, central Germany, Italian Peninsula and the island of Sicily, and in north-eastern Italy and northern Balkan regions (Dinaric Alps). Approximate Bayesian Computation simulations supported late Pleistocene-early Holocene population splittings (from c. 60 k to 10 k years ago), contemporary to the last Ice Age climatic changes. These results provide evidences for wildcat Mediterranean refuges in southwestern Europe, but the evolution history of eastern wildcat populations remains to be clarified. Historical genetic subdivisions suggest conservation strategies aimed at enhancing gene flow through the restoration of ecological corridors within each biogeographic units. Concomitantly, the risk of hybridization with free-ranging domestic cats along corridor edges should be carefully monitored

Risk-Averse Matchings over Uncertain Graph Databases

A large number of applications such as querying sensor networks, and analyzing protein-protein interaction (PPI) networks, rely on mining uncertain graph and hypergraph databases. In this work we study the following problem: given an uncertain, weighted (hyper)graph, how can we efficiently find a (hyper)matching with high expected reward, and low risk? This problem naturally arises in the context of several important applications, such as online dating, kidney exchanges, and team formation. We introduce a novel formulation for finding matchings with maximum expected reward and bounded risk under a general model of uncertain weighted (hyper)graphs that we introduce in this work. Our model generalizes probabilistic models used in prior work, and captures both continuous and discrete probability distributions, thus allowing to handle privacy related applications that inject appropriately distributed noise to (hyper)edge weights. Given that our optimization problem is NP-hard, we turn our attention to designing efficient approximation algorithms. For the case of uncertain weighted graphs, we provide a $\frac{1}{3}$-approximation algorithm, and a $\frac{1}{5}$-approximation algorithm with near optimal run time. For the case of uncertain weighted hypergraphs, we provide a $\Omega(\frac{1}{k})$-approximation algorithm, where $k$ is the rank of the hypergraph (i.e., any hyperedge includes at most $k$ nodes), that runs in almost (modulo log factors) linear time. We complement our theoretical results by testing our approximation algorithms on a wide variety of synthetic experiments, where we observe in a controlled setting interesting findings on the trade-off between reward, and risk. We also provide an application of our formulation for providing recommendations of teams that are likely to collaborate, and have high impact.Comment: 25 page