Massive amounts of useful data are stored and processed in ad hoc formats for which common tools like parsers, printers, query engines and format converters are not readily available. In this paper, we explain the design and implementation of PADS/ML, a new language and system that facilitates the generation of data processing tools for ad hoc formats. The PADS/ML design includes features such as dependent, polymorphic and recursive datatypes, which allow programmers to describe the syntax and semantics of ad hoc data in a concise, easy-to-read notation. The PADS/ML implementation compiles these descriptions into ML structures and functors that include types for parsed data, functions for parsing and printing, and auxiliary support for user-specified, format-dependent and format-independent tool generation.

Abstract. Domain-specific languages facilitate solving problems in a targeted domain by providing features particular to the domain. Declarative domain-specific languages have the additional benefit that users specify what something means rather than how to do something. As a result, the language compiler is free to choose the best implementation strategies and to generate multiple artifacts from a single description. PADS/ML is a declarative data description language designed to facilitate ad hoc data management. From a single description, the compiler generates a myriad of artifacts, including data structures for the in-memory representation of the data and parsers and printers. In this paper, we describe a new generic programming infrastructure for PADS/ML that allows third-party developers to define additional useful artifacts without modifying the compiler. We report on two case studies that use this infrastructure. In the first, we build a version of PADX for PADS/ML, allowing any data source with a PADS/ML description to be queried as if it were XML. In the second, we extend Harmony with the ability to synchronize any data with a PADS/ML description. 1

18 Institute for Advanced Study, Princeton and 19 Institut d’Astrophysique de Paris The GRavitational lEnsing Accuracy Testing 2008 (GREAT08) Challenge focuses on a problem that is of crucial importance for future observations in cosmology. The shapes of distant galaxies can

18 Institute for Advanced Study, Princeton and 19 Institut d’Astrophysique de Paris The GRavitational lEnsing Accuracy Testing 2008 (GREAT08) Challenge focuses on a problem that is of crucial importance for future observations in cosmology. The shapes of distant galaxies can be used to determine the properties of dark energy and the nature of gravity, because light from those galaxies is bent by gravity from the intervening dark matter. The observed galaxy images appear distorted, although only slightly, and their shapes must be precisely disentangled from the effects of pixelisation, convolution and noise. The worldwide gravitational lensing community has made significant progress in techniques to measure these distortions via the Shear TEsting Program (STEP). Via STEP, we have run challenges within our own community, and come to recognise that this particular image