The synthesis of nanocrystalline Fe3Al powder by mechanical alloying as well as the structural ordering of the synthesized Fe3Al particles during the subsequent thermal analysis were investigated. Mechanical alloying was performed up to 100 hours using a planetary ball mill apparatus with rotational speed of 300 rpm under argon atmosphere at ambient temperature. The synthesized powders were cha...

This article presents methods for the analysis and decomposition of multivariate datasets where a given ordering/structure of the observations or the variables exist. Examples of such data sets are remote sensing imagery where observations (pixels) each consisting of a reflectance spectrum are organised in a two-dimensional grid. Another example is biological shape analysis. Here each observati...

Directed acyclic graphs are commonly used to represent causal relationships among random variables in graphical models. Applications of these models arise in the study of physical and biological systems where directed edges between nodes represent the influence of components of the system on each other. Estimation of directed graphs from observational data is computationally NP-hard. In additio...

Most previous work on understanding variable ordering heuristics for constraint satisfaction problems has focused on the ability to recover from bad decisions. It has been demonstrated however that this ability cannot be the full explanation of the quality of a variable ordering heuristic [8]. In this paper, we develop a more complete framework for analyzing heuristics based on optimal policies...

In the last twenty years, many algorithms and heuristics were developed to find solutions in constraint networks. Their number increased to such an extent that it quickly became necessary to compare their performances in order to propose a small number of “good” methods. These comparisons often led us to consider FC or FC-CBJ associated with a “minimum domain” variable ordering heuristic as the...

In this work, we propose a scalable Bayesian procedure for learning the local dependence structure in high-dimensional model where variables possess natural ordering. The ordering of can be indexed by time, vicinities spatial locations, and so on, with assumption that far apart tend to have weak correlations. Applications such models abound variety fields as finance, genome associations analysi...