Palomar Testbed Interferometer: update

The Palomar Testbed Interferometer is a long-baseline near- infrared interferometer operating at Palomar Observatory, CA. The interferometer has a maximum baseline of 110 m, 40-cm collecting apertures, and active fringe tracking. It also incorporates a dual-star architecture to enable cophasing and narrow-angle astrometry. We will discuss recent system improvements and engineering results. These include upgrades to allow for longer coherent integration times, H band operation, and cophasing using delay line feedforward. Recent engineering tests of astrometry in dual-star mode have shown a night-to-night repeatability of 100 µas on a bright test target. Several new observation planning tools have been developed, and data reduction tools have been automated to allow fully pipelined nightly reductions and archiving

Creativity and Autonomy in Swarm Intelligence Systems

This work introduces two swarm intelligence algorithms -- one mimicking the behaviour of one species of ants (\emph{Leptothorax acervorum}) foraging (a `Stochastic Diffusion Search', SDS) and the other algorithm mimicking the behaviour of birds flocking (a `Particle Swarm Optimiser', PSO) -- and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploliting an artistic tension between the local behaviour of the `birds flocking' - as they seek to follow the input sketch - and the global behaviour of the `ants foraging' - as they seek to encourage the flock to explore novel regions of the canvas. The paper concludes by exploring the putative `creativity' of this hybrid swarm system in the philosophical light of the `rhizome' and Deleuze's well known `Orchid and Wasp' metaphor

Moebius strip enterprises and expertise in the creative industries: new challenges for lifelong learning?

The paper argues that the emergence of a new mode of production – co-configuration is generating new modes of expertise that EU policies for lifelong learning are not designed to support professionals to develop. It maintains that this change can be seen most clearly when we analyse Small and Medium Size (SMEs) enterprises in the creative industries. Drawing on concepts from Political Economy - ‘Moebius strip enterprise/expertise’ and Cultural Historical Activity Theory - project-object’ and the ‘space of reasons’, the paper highlights conceptually and through a case study of an SME in the creative industries what is distinctive about the new modes of expertise, before moving on to reconceptualise expertise and learning and to consider the implications of this reconceptualisation for EU policies for lifelong learning. The paper concludes that the new challenge for LLL is to support the development of new forms expertise that are difficult to credentialise, yet, are central to the wider European goal of realising a knowledge economy

Ranking deep web text collections for scalable information extraction

Information extraction (IE) systems discover structured in-formation from natural language text, to enable much richer querying and data mining than possible directly over the unstructured text. Unfortunately, IE is generally a com-putationally expensive process, and hence improving its ef-ficiency, so that it scales over large volumes of text, is of critical importance. State-of-the-art approaches for scaling the IE process focus on one text collection at a time. These approaches prioritize the extraction effort by learning key-word queries to identify the “useful ” documents for the IE task at hand, namely, those that lead to the extraction of structured “tuples. ” These approaches, however, do not at-tempt to predict which text collections are useful for the IE task—and hence merit further processing—and which ones will not contribute any useful output—and hence should be ignored altogether, for efficiency. In this paper, we focus on an especially valuable family of text sources, the so-called deep web collections, whose (remote) contents are only ac-cessible via querying. Specifically, we introduce and study techniques for ranking deep web collections for an IE task, to prioritize the extraction effort by focusing on collections with substantial numbers of useful documents for the task. We study both (adaptations of) state-of-the-art resource se-lection strategies for distributed information retrieval, and IE-specific approaches. Our extensive experimental eval-uation over realistic deep web collections, and for several different IE tasks, shows the merits and limitations of the alternative families of approaches, and provides a roadmap for addressing this critically important building block for efficient, scalable information extraction. 1

Some Empirical Criteria for Attributing Creativity to a Computer Program

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