Phases and phase stabilities of Fe3X alloys (X=Al, As, Ge, In, Sb, Si, Sn, Zn) prepared by mechanical alloying

Mechanical alloying with a Spex 8000 mixer/mill was used to prepare several alloys of the Fe3X composition, where the solutes X were from groups IIB, IIIB, IVB, and VB of the periodic table. Using x-ray diffractometry and Mössbauer spectrometry, we determined the steady-state phases after milling for long times. The tendencies of the alloys to form the bcc phase after milling are predicted well with the modified usage of a Darken–Gurry plot of electronegativity versus metallic radius. Thermal stabilities of some of these phases were studied. In the cases of Fe3Ge and Fe3Sn, there was the formation of transient D03 and B2 order during annealing, although this ordered structure was replaced by equilibrium phases upon further annealing

Luminous Infrared Galaxies in the Local Universe

We study the morphology and star formation properties of 159 local luminous infrared galaxy (LIRG) using multi-color images from Data Release 2 (DR2) of the Sloan Digital Sky Survey (SDSS). The LIRGs are selected from a cross-correlation analysis between the IRAS survey and SDSS. They are all brighter than 15.9 mag in the r-band and below redshift ~ 0.1, and so can be reliably classified morphologically. We find that the fractions of interacting/merging and spiral galaxies are ~ 48% and ~ 40% respectively. Our results complement and confirm the decline (increase) in the fraction of spiral (interacting/merging) galaxies from z ~1 to z ~ 0.1, as found by Melbourne, Koo & Le Floc'h (2005). About 75% of spiral galaxies in the local LIRGs are barred, indicating that bars may play an important role in triggering star formation rates > 20 M_{sun}/yr in the local universe. Compared with high redshift LIRGs, local LIRGs have lower specific star formation rates, smaller cold gas fractions and a narrower range of stellar masses. Local LIRGs appear to be either merging galaxies forming intermediate mass ellipticals or spiral galaxies undergoing high star formation activities regulated by bars.Comment: 22 pages, 5 figures, accepted for publication in ApJ, title changed, typos corrected,major revisions following referee's comments,updated reference

Multi-graph-view subgraph mining for graph classification

© 2015, Springer-Verlag London. In this paper, we formulate a new multi-graph-view learning task, where each object to be classified contains graphs from multiple graph-views. This problem setting is essentially different from traditional single-graph-view graph classification, where graphs are collected from one single-feature view. To solve the problem, we propose a cross graph-view subgraph feature-based learning algorithm that explores an optimal set of subgraphs, across multiple graph-views, as features to represent graphs. Specifically, we derive an evaluation criterion to estimate the discriminative power and redundancy of subgraph features across all views, with a branch-and-bound algorithm being proposed to prune subgraph search space. Because graph-views may complement each other and play different roles in a learning task, we assign each view with a weight value indicating its importance to the learning task and further use an optimization process to find optimal weight values for each graph-view. The iteration between cross graph-view subgraph scoring and graph-view weight updating forms a closed loop to find optimal subgraphs to represent graphs for multi-graph-view learning. Experiments and comparisons on real-world tasks demonstrate the algorithm’s superior performance

Multi-graph learning with positive and unlabeled bags

© SIAM. In this paper, we formulate a new multi-graph learning task with only positive and unlabeled bags, where labels are only available for bags but not for individual graphs inside the bag. This problem setting raises significant challenges because bag-of-graph setting does not have features to directly represent graph data, and no negative bags exits for deriving discriminative classification models. To solve the challenge, we propose a puMGL learning framework which relies on two iteratively combined processes for multigraph learning: (1) deriving features to represent graphs for learning; and (2) deriving discriminative models with only positive and unlabeled graph bags. For the former, we derive a subgraph scoring criterion to select a set of informative subgraphs to convert each graph into a feature space. To handle unlabeled bags, we assign a weight value to each bag and use the adjusted weight values to select most promising unlabeled bags as negative bags. A margin graph pool (MGP), which contains some representative graphs from positive bags and identified negative bags, is used for selecting subgraphs and training graph classifiers. The iterative subgraph scoring, bag weight updating, and MGP based graph classification forms a closed loop to find optimal subgraphs and most suitable unlabeled bags for multi-graph learning. Experiments and comparisons on real-world multigraph data demonstrate the algorithm performance. Copyrigh

Chaos control in random Boolean networks by reducing mean damage percolation rate

Chaos control in Random Boolean networks is implemented by freezing part of the network to drive it from chaotic to ordered phase. However, controlled nodes are only viewed as passive blocks to prevent perturbation spread. This paper proposes a new control method in which controlled nodes can exert an active impact on the network. Controlled nodes and frozen values are deliberately selected according to the information of connection and Boolean functions. Simulation results show that the number of nodes needed to achieve control is largely reduced compared to previous method. Theoretical analysis is also given to estimate the least fraction of nodes needed to achieve control.Comment: 10 pages, 2 figure