Autonomy in the global era: Euro-regionalism and new policy spaces in education. ACES Working Papers, August 2010

Framed by a critical discussion of methodological nationalism, this paper explores the intersection of new and evolving regional, central state, and supranational education policy spaces through examples drawn from post-Franco Spain. This work is situated within the broader literature on the development of a European Education Policy Space, which aims to understand changing governance structures in European education (cf. Grek et al., 2009; Lawn & Lingard,2002; N6voa & Lawn, 2002). Using policy documents since 2000 and interview data, the paper first examines Spanish and regional (Catalan) education policy related to devolution, namely Catalonia's recently revised Statute of Autonomy. The paper then places devolution in Spain and Catalonia in a broader context of Euro-regionalism, which has deepened and legitimized regional autonomy. Together these shifts in educational governance and the development of new education policy spaces have promoted a concept of the multi-scalar, European "ideal citizen" (Engel & Ortloff, 2009). The last section presents an overview of the recent influx of immigrants into Catalonia and Spain, exploring whether and to what extent recent education policy promoting the "ideal citizen" has taken non-European immigrants into account.

A planar geometrical model for representing multidimensional discrete spaces and multiple-valued logic functions /

Bibliography: p. 37-38.

The selection of preferred metric values for design and construction /

Sponsored by Naval Facilities Engineering Command, Department of the Navy.

Metric conversion in the construction industries--technical issues and status /

Mode of access: Internet.

Vestiges of pre-metric weights and measures persisting in metric-system Europe, 1926-1927.

Half-title: Bureau of international research, Harvard university and Radcliffe college.

Providing a metric option : can laws and regulations be amended in a timely manner?

Item 1089

The Art of the Contemporary Anti-Racist American Poem: ‘In-Between Spaces,’ Exploding Conventions, and Listening as Form

Thesis (Master's)--University of Washington, 2016-06

Playing in continuous spaces: Some analysis and extension of population-based incremental learning

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.

Topographic mappings and feed-forward neural networks

This thesis is a study of the generation of topographic mappings - dimension reducing transformations of data that preserve some element of geometric structure - with feed-forward neural networks. As an alternative to established methods, a transformational variant of Sammon's method is proposed, where the projection is effected by a radial basis function neural network. This approach is related to the statistical field of multidimensional scaling, and from that the concept of a 'subjective metric' is defined, which permits the exploitation of additional prior knowledge concerning the data in the mapping process. This then enables the generation of more appropriate feature spaces for the purposes of enhanced visualisation or subsequent classification. A comparison with established methods for feature extraction is given for data taken from the 1992 Research Assessment Exercise for higher educational institutions in the United Kingdom. This is a difficult high-dimensional dataset, and illustrates well the benefit of the new topographic technique. A generalisation of the proposed model is considered for implementation of the classical multidimensional scaling (¸mds}) routine. This is related to Oja's principal subspace neural network, whose learning rule is shown to descend the error surface of the proposed ¸mds model. Some of the technical issues concerning the design and training of topographic neural networks are investigated. It is shown that neural network models can be less sensitive to entrapment in the sub-optimal global minima that badly affect the standard Sammon algorithm, and tend to exhibit good generalisation as a result of implicit weight decay in the training process. It is further argued that for ideal structure retention, the network transformation should be perfectly smooth for all inter-data directions in input space. Finally, there is a critique of optimisation techniques for topographic mappings, and a new training algorithm is proposed. A convergence proof is given, and the method is shown to produce lower-error mappings more rapidly than previous algorithms.