The Spectral and Photometric Imaging REceiver (SPIRE) is one of the three scientific instruments to fly on the European Space Agency's Herschel Space Observatory, and contains a three-band imaging submillimetre photometer and an imaging Fourier transform spectrometer. The flight model of the SPIRE cold focal plane unit has been built up in stages with a cold test campaign associated with each stage. The first campaign focusing on the spectrometer took place in early 2005 and the second campaign focusing on the photometer was in Autumn 2005. SPIRE is currently undergoing its third cold test campaign following cryogenic vibration testing. Test results to date show that the instrument is performing very well and in general meets not only its requirements but also most of its performance goals. We present an overview of the instrument tests performed to date, and the preliminary results.

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SPIRE is one of three instruments on board ESA's Herschel space observatory, due for launch in 2008. The instrument comprises both a photometer and Fourier transform spectrometer. The Herschel mission has a limited operational lifetime of 3.5 years and, as with all space-based facilities, has very high development and operational costs. As a result observing time is a valuable and limited resource, making efficiency of crucial importance. In this paper we present recent results derived from the SPIRE photometer simulator, detailing the optimum observing mode parameters to be employed by the Herschel/SPIRE system. We also outline the efficiency of various modes, leading to the conclusion that scan mapping is the optimal mode for the mapping of regions greater than ∼4 × 4 .

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players' and coordinated teams' behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-theart and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).