The embodied hybrid space : designing for digital encounters in physical environments

The emergence of mobile and ubiquitous computing has created what is referred to as a hybrid space – a virtual layer of digital information and interaction opportunities that sits on top and augments the physical environment. The increasing connectedness through such media, from anywhere to anybody at anytime, makes us less dependent on being physically present somewhere in particular. But, what is the role of ubiquitous computing in making physical presence at a particular place more attractive? Acknowledging historic context and identity as important attributes of place, this work embarks on a ‘global sense of place’ in which the cultural diversity, multiple identities, backgrounds, skills and experiences of people traversing a place are regarded as social assets of that place. The aim is to explore ways how physical architecture and infrastructure of a place can be mediated towards making invisible social assets visible, thus augmenting people’s situated social experience. Thereby, the focus is on embodied media, i.e. media that materialise digital information as observable and sometimes interactive parts of the physical environment hence amplify people’s real world experience, rather than substituting or moving it to virtual spaces.

Space : Irrealis objects and their role in a new political economy

In this paper, I show how new spaces are being prefigured for colonisation in the language of contemporary technology policy. Drawing on a corpus of 1.3 million words collected from technology policy centres throughout the world, I show the role of policy language in creating the foundations of an emergent form of political economy. The analysis is informed by principles from critical discourse analysis (CDA) and classical political economy. It foregrounds a functional aspect of language called process metaphor to show how aspects of human activity are prefigured for mass commodification by the manipulation of irrealis spaces. I also show how the fundamental element of any new political economy, the property element, is being largely ignored. The potential creation of a global space as concrete as landed property – electromagnetic spectrum – has significant ramifications for the future of social relations in any global “knowledge economy”.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Multiview point cloud kernels for semisupervised learning [Lecture Notes]

In semisupervised learning (SSL), a predictive model is learn from a collection of labeled data and a typically much larger collection of unlabeled data. These paper presented a framework called multi-view point cloud regularization (MVPCR), which unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHSs). Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multi-view kernel.

Ceremony and cybrary : Digital libraries and the dialectic of place and space

This article examines social, cultural and technological change in the systems and economies of educational information management. Since the Sumerians first collected, organized and supervised administrative and religious records some six millennia ago, libraries have been key physical depositories and cultural signifiers in the production and mediation of social capital and power through education. To date, the textual, archival and discursive practices perpetuating libraries have remained exempt from inquiry. My aim here is to remedy this hiatus by making the library itself the terrain and object of critical analysis and investigation. The paper argues that in the three dominant communications eras—namely, oral, print and digital cultures—society’s centres of knowledge and learning have resided in the ceremony, the library and the cybrary respectively. In a broad-brush historical grid, each of these key educational institutions—the ceremony in oral culture, the library in print culture and the cybrary in digital culture—are mapped against social, cultural and technological orders pertaining to their era. Following a description of these shifts in society’s collective cultural memory, the paper then examines the question of what the development of global information systems and economies mean for schools and libraries of today, and for teachers and learners as knowledge consumers and producers?

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

A review of natural convection and heat transfer in attic-shaped space

This paper presents a review of studies on natural convection heat transfer in the triangular enclosure namely, in attic-shaped space. Much research activity has been devoted to this topic over the last three decades with a view to providing thermal comfort to the occupants in attic-shaped buildings and to minimising the energy costs associated with heating and air-conditioning. Two basic thermal boundary conditions of attic are considered to represent hot and cold climates or day and night time. This paper also reports on a significant number of studies which have been performed recently on other topics related to the attic space, for example, attics subject to localized heating and attics filled with porous media.

Natural convection adjacent to an inclined flat plate and in an attic space under various thermal forcing conditions

The effect of viscous dissipation on natural convection from a vertical plate placed in a thermally stratified environment has been investigated numerically. The reduced equations are integrated by employing the implicit finite difference scheme or Ke1ler-box method and obtained the effect of heat due to viscous dissipation on the local skin-friction and loca1 Nusselt number at various stratification levels, for fluids having Prandtl number equals 10, 50, and 100. Solutions are also obtained using the perturbation technique for small values of viscous dissipation parameters and compared with the Finite Difference solutions. Effect of the heat transfer due to viscous dissipation and the temperature stratification are also shown on the velocity and temperature distributions in the boundary layer region. A numerical study of laminar doubly diffusive free convection flows adjacent to a vertical surface in a stable thermally stratified medium is also considered for this study. Solutions are obtained using the implicit Finite Difference method and compared with the local non-similarity method. The velocity and temperature distributions for different values of stratification parameter are shown graphically. The results show many interesting aspects of complex interaction of the two buoyant mechanisms.

Auditory spatial acuity approximates the resolving power of space-specific neurons

The relationship between neuronal acuity and behavioral performance was assessed in the barn owl (Tyto alba), a nocturnal raptor renowned for its ability to localize sounds and for the topographic representation of auditory space found in the midbrain. We measured discrimination of sound-source separation using a newly developed procedure involving the habituation and recovery of the pupillary dilation response. The smallest discriminable change of source location was found to be about two times finer in azimuth than in elevation. Recordings from neurons in its midbrain space map revealed that their spatial tuning, like the spatial discrimination behavior, was also better in azimuth than in elevation by a factor of about two. Because the PDR behavioral assay is mediated by the same circuitry whether discrimination is assessed in azimuth or in elevation, this difference in vertical and horizontal acuity is likely to reflect a true difference in sensory resolution, without additional confounding effects of differences in motor performance in the two dimensions. Our results, therefore, are consistent with the hypothesis that the acuity of the midbrain space map determines auditory spatial discrimination.

Predicting residue-wise contact orders in proteins by support vector regression

Background The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.