Knowledge in (Geo)Visualisation: The relationship between seeing and thinking

Modern research in geovisualisation has framed the discipline as a field more akin to geovisual analytics - one that pleaces an emphasis on the human elements of exploration of data through interactive and dynamic geo-interfaces, rather than simple data representation. This rephrasing highlights the importance of cognitive aspects of human interaction with geo-based data and the interfaces designed to present them. In an attempt to provide a psychological background to the benefits of geovisual analytics, this paper will explroe the role that perception hasi n complex problem solving and knowledge discovery, and will demonstrate that, through modern interactive technologies, (geo)visualiations augment and facilitate our natural ability to surface novel, surprising and otherwise invisible relationships between information. It will argue that it is through these novel relationships that we add to our understandings of the original information and simultaneously reveal new knowledge 'between the gaps'

Mapping the landscape of climate engineering.

In the absence of a governance framework for climate engineering technologies such as solar radiation management (SRM), the practices of scientific research and intellectual property acquisition can de facto shape the development of the field. It is therefore important to make visible emerging patterns of research and patenting, which we suggest can effectively be done using bibliometric methods. We explore the challenges in defining the boundary of climate engineering, and set out the research strategy taken in this study. A dataset of 825 scientific publications on climate engineering between 1971 and 2013 was identified, including 193 on SRM; these are analysed in terms of trends, institutions, authors and funders. For our patent dataset, we identified 143 first filings directly or indirectly related to climate engineering technologies-of which 28 were related to SRM technologies-linked to 910 family members. We analyse the main patterns discerned in patent trends, applicants and inventors. We compare our own findings with those of an earlier bibliometric study of climate engineering, and show how our method is consistent with the need for transparency and repeatability, and the need to adjust the method as the field develops. We conclude that bibliometric monitoring techniques can play an important role in the anticipatory governance of climate engineering

Einstein-Maxwell gravitational instantons and five dimensional solitonic strings

We study various aspects of four dimensional Einstein-Maxwell multicentred gravitational instantons. These are half-BPS Riemannian backgrounds of minimal N=2 supergravity, asymptotic to R^4, R^3 x S^1 or AdS_2 x S^2. Unlike for the Gibbons-Hawking solutions, the topology is not restricted by boundary conditions. We discuss the classical metric on the instanton moduli space. One class of these solutions may be lifted to causal and regular multi `solitonic strings', without horizons, of 4+1 dimensional N=2 supergravity, carrying null momentum.Comment: 1+30 page

Beyond Hebb: Exclusive-OR and Biological Learning

A learning algorithm for multilayer neural networks based on biologically plausible mechanisms is studied. Motivated by findings in experimental neurobiology, we consider synaptic averaging in the induction of plasticity changes, which happen on a slower time scale than firing dynamics. This mechanism is shown to enable learning of the exclusive-OR (XOR) problem without the aid of error back-propagation, as well as to increase robustness of learning in the presence of noise.Comment: 4 pages RevTeX, 2 figures PostScript, revised versio

Learning from M/EEG data with variable brain activation delays

International audienceMagneto- and electroencephalography (M/EEG) measure the electromagnetic signals produced by brain activity. In order to address the issue of limited signal-to-noise ratio (SNR) with raw data, acquisitions consist of multiple repetitions of the same experiment. An important challenge arising from such data is the variability of brain activations over the repetitions. It hinders statistical analysis such as prediction performance in a supervised learning setup. One such confounding variability is the time offset of the peak of the activation, which varies across repetitions. We propose to address this misalignment issue by explicitly modeling time shifts of different brain responses in a classification setup. To this end, we use the latent support vector machine (LSVM) formulation, where the latent shifts are inferred while learning the classifier parameters. The inferred shifts are further used to improve the SNR of the M/EEG data, and to infer the chronometry and the sequence of activations across the brain regions that are involved in the experimental task. Results are validated on a long term memory retrieval task, showing significant improvement using the proposed latent discriminative method

Gravitational Entropy and Global Structure

The underlying reason for the existence of gravitational entropy is traced to the impossibility of foliating topologically non-trivial Euclidean spacetimes with a time function to give a unitary Hamiltonian evolution. In $d$ dimensions the entropy can be expressed in terms of the $d-2$ obstructions to foliation, bolts and Misner strings, by a universal formula. We illustrate with a number of examples including spaces with nut charge. In these cases, the entropy is not just a quarter the area of the bolt, as it is for black holes.Comment: 18 pages. References adde

Cycle-based Cluster Variational Method for Direct and Inverse Inference

We elaborate on the idea that loop corrections to belief propagation could be dealt with in a systematic way on pairwise Markov random fields, by using the elements of a cycle basis to define region in a generalized belief propagation setting. The region graph is specified in such a way as to avoid dual loops as much as possible, by discarding redundant Lagrange multipliers, in order to facilitate the convergence, while avoiding instabilities associated to minimal factor graph construction. We end up with a two-level algorithm, where a belief propagation algorithm is run alternatively at the level of each cycle and at the inter-region level. The inverse problem of finding the couplings of a Markov random field from empirical covariances can be addressed region wise. It turns out that this can be done efficiently in particular in the Ising context, where fixed point equations can be derived along with a one-parameter log likelihood function to minimize. Numerical experiments confirm the effectiveness of these considerations both for the direct and inverse MRF inference.Comment: 47 pages, 16 figure

Emergence of memory

We propose a new self-organizing mechanism behind the emergence of memory in which temporal sequences of stimuli are transformed into spatial activity patterns. In particular, the memory emerges despite the absence of temporal correlations in the stimuli. This suggests that neural systems may prepare a spatial structure for processing information before the information itself is available. A simple model illustrating the mechanism is presented based on three principles: (1) Competition between neural units, 2) Hebbian plasticity, and (3) recurrent connections.Comment: 7 pages, 4 figures, EPL styl

Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning

Optimization problems with an auxiliary latent variable structure in addition to the main model parameters occur frequently in computer vision and machine learning. The additional latent variables make the underlying optimization task expensive, either in terms of memory (by maintaining the latent variables), or in terms of runtime (repeated exact inference of latent variables). We aim to remove the need to maintain the latent variables and propose two formally justified methods, that dynamically adapt the required accuracy of latent variable inference. These methods have applications in large scale robust estimation and in learning energy-based models from labeled data.Comment: 16 page

Analysis of CHK2 in vulval neoplasia

Structure and expression of the Rad53 homologue CHK2 were studied in vulval neoplasia. We identified the previously described silent polymorphism at codon 84 (A>G at nucleotide 252) in the germ-line of six out of 72, and somatic mutations in two out of 40 cases of vulval squamous cell carcinomas and none of 32 cases of vulval intraepithelial neoplasia. One mutation introduced a premature stop codon in the kinase domain of CHK2, whereas the second resulted in an amino acid substitution in the kinase domain. The two squamous cell carcinomas with mutations in CHK2 also expressed mutant p53. A CpG island was identified close to the putative CHK2 transcriptional start site, but methylation-specific PCR did not detect methylation in any of 40 vulval squamous cell carcinomas, irrespective of human papillomavirus or p53 status. Consistent with this observation, no cancer exhibited loss of CHK2 expression at mRNA or protein level. Taken together, these observations reveal that genetic but not epigenetic changes in CHK2 occur in a small proportion of vulval squamous cell carcinomas