Natural language processing as a foundation of the semantic Web

The main argument of this paper is that Natural Language Processing (NLP) does, and will continue to, underlie the Semantic Web (SW), including its initial construction from unstructured sources like the World Wide Web (WWW), whether its advocates realise this or not. Chiefly, we argue, such NLP activity is the only way up to a defensible notion of meaning at conceptual levels (in the original SW diagram) based on lower level empirical computations over usage. Our aim is definitely not to claim logic-bad, NLP-good in any simple-minded way, but to argue that the SW will be a fascinating interaction of these two methodologies, again like the WWW (which has been basically a field for statistical NLP research) but with deeper content. Only NLP technologies (and chiefly information extraction) will be able to provide the requisite RDF knowledge stores for the SW from existing unstructured text databases in the WWW, and in the vast quantities needed. There is no alternative at this point, since a wholly or mostly hand-crafted SW is also unthinkable, as is a SW built from scratch and without reference to the WWW. We also assume that, whatever the limitations on current SW representational power we have drawn attention to here, the SW will continue to grow in a distributed manner so as to serve the needs of scientists, even if it is not perfect. The WWW has already shown how an imperfect artefact can become indispensable.

Form follows function:model-driven engineering for clinical trials

We argue that, for certain constrained domains, elaborate model transformation technologies-implemented from scratch in general-purpose programming languages-are unnecessary for model-driven engineering; instead, lightweight configuration of commercial off-the-shelf productivity tools suffices. In particular, in the CancerGrid project, we have been developing model-driven techniques for the generation of software tools to support clinical trials. A domain metamodel captures the community's best practice in trial design. A scientist authors a trial protocol, modelling their trial by instantiating the metamodel; customized software artifacts to support trial execution are generated automatically from the scientist's model. The metamodel is expressed as an XML Schema, in such a way that it can be instantiated by completing a form to generate a conformant XML document. The same process works at a second level for trial execution: among the artifacts generated from the protocol are models of the data to be collected, and the clinician conducting the trial instantiates such models in reporting observations-again by completing a form to create a conformant XML document, representing the data gathered during that observation. Simple standard form management tools are all that is needed. Our approach is applicable to a wide variety of information-modelling domains: not just clinical trials, but also electronic public sector computing, customer relationship management, document workflow, and so on. © 2012 Springer-Verlag.

Regrouping metric-space search index for search engine size adaptation

This work contributes to the development of search engines that self-adapt their size in response to fluctuations in workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computational resources to or from the engine. In this paper, we focus on the problem of regrouping the metric-space search index when the number of virtual machines used to run the search engine is modified to reflect changes in workload. We propose an algorithm for incrementally adjusting the index to fit the varying number of virtual machines. We tested its performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud, while calibrating the results to compensate for the performance fluctuations of the platform. Our experiments show that, when compared with computing the index from scratch, the incremental algorithm speeds up the index computation 2–10 times while maintaining a similar search performance.

Self-adapting parallel metric-space search engine for variable query loads

This research focuses on automatically adapting a search engine size in response to fluctuations in query workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computer resources to or from the engine. Our solution is to contribute an adaptive search engine that will repeatedly re-evaluate its load and, when appropriate, switch over to a dierent number of active processors. We focus on three aspects and break them out into three sub-problems as follows: Continually determining the Number of Processors (CNP), New Grouping Problem (NGP) and Regrouping Order Problem (ROP). CNP means that (in the light of the changes in the query workload in the search engine) there is a problem of determining the ideal number of processors p active at any given time to use in the search engine and we call this problem CNP. NGP happens when changes in the number of processors are determined and it must also be determined which groups of search data will be distributed across the processors. ROP is how to redistribute this data onto processors while keeping the engine responsive and while also minimising the switchover time and the incurred network load. We propose solutions for these sub-problems. For NGP we propose an algorithm for incrementally adjusting the index to t the varying number of virtual machines. For ROP we present an ecient method for redistributing data among processors while keeping the search engine responsive. Regarding the solution for CNP, we propose an algorithm determining the new size of the search engine by re-evaluating its load. We tested the solution performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud. Our experiments show that when we compare our NGP solution with computing the index from scratch, the incremental algorithm speeds up the index computation 2{10 times while maintaining a similar search performance. The chosen redistribution method is 25% to 50% faster than other methods and reduces the network load around by 30%. For CNP we present a deterministic algorithm that shows a good ability to determine a new size of search engine. When combined, these algorithms give an adapting algorithm that is able to adjust the search engine size with a variable workload.

Random walk with restart over dynamic graphs

Random Walk with Restart (RWR) is an appealing measure of proximity between nodes based on graph structures. Since real graphs are often large and subject to minor changes, it is prohibitively expensive to recompute proximities from scratch. Previous methods use LU decomposition and degree reordering heuristics, entailing O(|V|^3) time and O(|V|^2) memory to compute all (|V|^2) pairs of node proximities in a static graph. In this paper, a dynamic scheme to assess RWR proximities is proposed: (1) For unit update, we characterize the changes to all-pairs proximities as the outer product of two vectors. We notice that the multiplication of an RWR matrix and its transition matrix, unlike traditional matrix multiplications, is commutative. This can greatly reduce the computation of all-pairs proximities from O(|V|^3) to O(|delta|) time for each update without loss of accuracy, where |delta| (<<|V|^2) is the number of affected proximities. (2) To avoid O(|V|^2) memory for all pairs of outputs, we also devise efficient partitioning techniques for our dynamic model, which can compute all pairs of proximities segment-wisely within O(l|V|) memory and O(|V|/l) I/O costs, where 1<=l<=|V| is a user-controlled trade-off between memory and I/O costs. (3) For bulk updates, we also devise aggregation and hashing methods, which can discard many unnecessary updates further and handle chunks of unit updates simultaneously. Our experimental results on various datasets demonstrate that our methods can be 1–2 orders of magnitude faster than other competitors while securing scalability and exactness.

Crystal orientation effects in scratch testing with a spherical indenter

Spherical scratch tests were conducted in individual grains of a randomly oriented polycrystalline body-centered-cubic (bcc) Ti-Nb alloy. For each grain, scratch tests were conducted at four different levels of normal load, which resulted in varying amounts of plastic strain during indentation. The results show a dependence of the horizontal load component on the crystallographic orientation and on the amount of plastic strain. The component of the horizontal force that resulted from plastic deformation was found to correlate with the active slip systems for the particular grain orientation. © 2010 Materials Research Society.

Mesenchymal stem cell-conditioned medium accelerates skin wound healing:an in vitro study of fibroblast and keratinocyte scratch assays

We have used in vitro scratch assays to examine the relative contribution of dermal fibroblasts and keratinocytes in the wound repair process and to test the influence of mesenchymal stem cell (MSC) secreted factors on both skin cell types. Scratch assays were established using single cell and co-cultures of L929 fibroblasts and HaCaT keratinocytes, with wound closure monitored via time-lapse microscopy. Both in serum supplemented and serum free conditions, wound closure was faster in L929 fibroblast than HaCaT keratinocyte scratch assays, and in co-culture the L929 fibroblasts lead the way in closing the scratches. MSC-CM generated under serum free conditions significantly enhanced the wound closure rate of both skin cell types separately and in co-culture, whereas conditioned medium from L929 or HaCaT cultures had no significant effect. This enhancement of wound closure in the presence of MSC-CM was due to accelerated cell migration rather than increased cell proliferation. A number of wound healing mediators were identified in MSC-CM, including TGF-beta1, the chemokines IL-6, IL-8, MCP-1 and RANTES, and collagen type I, fibronectin, SPARC and IGFBP-7. This study suggests that the trophic activity of MSC may play a role in skin wound closure by affecting both dermal fibroblast and keratinocyte migration, along with a contribution to the formation of extracellular matrix.

Graphene layers produced from carbon nanotubes by frication

Graphene layers have been produced from multi-walled carbon nanotube (MWCNT) bulk materials by friction when polished on ground-glass, offering a novel and effective method to produce graphene layers, which, more importantly, could be transferred to other substrates by rubbing. Field emission scanning electron microscopy, Raman spectroscopy, atomic force microscopy, transmission electron microscopy and selected area electron diffraction confirmed the formation of graphene layers. They were thought to be peeled away from the MWCNT walls due to friction. The reflection spectra showed that absorption of as-produced graphene layers decreased with wavelength in the range of 250–400 nm, compared to the MWCNT bulk material having strong absorption at 350 nm. Nanoscratch test was used to determine the mechanical properties of graphene films, suggesting the tolerance of as-produced graphene film to flaws introduced by scratch.