To fit in or stand out? The interaction between mortality salience, dissent and self esteem on social projection

Social projection (SP) refers to our tendency to assume that others think the same as we do, but the effect can also be used to detect the extent to which participants want to see themselves as similar to others. Simon et al (1997) found that participants informed that they were deviant increased their SP but those told that they were conformist reduced theirs. This compensatory function supports Brewer’s optimal distinctiveness which states that a balance must be struck between competing desires to feel similar and unique. In line with terror management theory, the effect was particularly apparent under conditions of mortality salience (MS). So far SP has only been examined on measures that target personal identity so this experiment developed a measure to target social identity as well. Participants were provided with either minority or majority dissent feedback, in MS or control conditions, and their SP on items relevant to personal and social identity were recorded. Results showed that group feedback only impacted upon participants SP on social identity measures and interacted with MS and self-esteem; those with high self-esteem had higher SP scores following minority dissent under conditions of mortality salience, indicating an attempt to assert their individuality. On SP measures targeting personal identity, MS and self-esteem interacted; the death prime increased SP scores for those with low self-esteem but decreased it for those with high self-esteem. Findings are interpreted in terms of TMT and optimal distinctiveness theory and their applications.

A critical examination of node-similarity based graph matching algorithms

In this paper, we shall critically examine a special class of graph matching algorithms that follow the approach of node-similarity measurement. A high-level algorithm framework, namely node-similarity graph matching framework (NSGM framework), is proposed, from which, many existing graph matching algorithms can be subsumed, including the eigen-decomposition method of Umeyama, the polynomial-transformation method of Almohamad, the hubs and authorities method of Kleinberg, and the kronecker product successive projection methods of Wyk, etc. In addition, improved algorithms can be developed from the NSGM framework with respects to the corresponding results in graph theory. As the observation, it is pointed out that, in general, any algorithm which can be subsumed from NSGM framework fails to work well for graphs with non-trivial auto-isomorphism structure.

Multilevel refinement for combinatorial optimisation: boosting metaheuristic performance

The multilevel paradigm as applied to combinatorial optimisation problems is a simple one, which at its most basic involves recursive coarsening to create a hierarchy of approximations to the original problem. An initial solution is found, usually at the coarsest level, and then iteratively refined at each level, coarsest to finest, typically by using some kind of heuristic optimisation algorithm (either a problem-specific local search scheme or a metaheuristic). Solution extension (or projection) operators can transfer the solution from one level to another. As a general solution strategy, the multilevel paradigm has been in use for many years and has been applied to many problem areas (for example multigrid techniques can be viewed as a prime example of the paradigm). Overview papers such as [] attest to its efficacy. However, with the exception of the graph partitioning problem, multilevel techniques have not been widely applied to combinatorial problems and in this chapter we discuss recent developments. In this chapter we survey the use of multilevel combinatorial techniques and consider their ability to boost the performance of (meta)heuristic optimisation algorithms.

Three-dimensional simulation of the influence of convection on dendritic solidification

Bulk and interdendritic flow during solidification alters the microstructure development, potentially leading to the formation of defects. In this paper, a 3D numerical model is presented for the simulation of dendritic growth in the presence of fluid flow in both liquid and semi-solid zones during solidification. The dendritic growth was solved by the combination of a stochastic nucleation approach with a finite difference solution of the solute diffusion equation and. a projection method solution of the Navier-Stokes equations. The technique was applied first to simulate the growth of a single dendrite in 2D and 3D in an isothermal environment with forced fluid flow. Significant differences were found in the evolution of dendritic morphology when comparing the 2D and 3D results. In 3D the upstream arm has a faster growth velocity due to easier flow around the perpendicular arms. This also promotes secondary arm formation on the upstream arm. The effect of fluid flow on columnar dendritic growth and micro-segregation in constrained solidification conditions is then simulated. For constrained growth, 2D simulations lead to even greater inaccuracies as compared to 3D.