The French Constitutional Council as the Rottweiler of the Republican Ideal in the Language Field: Does Jurisprudence Really Reflect Reality? EDAP 03/2012, 2012

France is known for being a champion of individual rights as well as for its overt hostility to any form of group rights. Linguistic pluralism in the public sphere is rejected for fear of babelization and Balkanization of the country. Over recent decades the Conseil Constitutionnel (CC) has, together with the Conseil d’État, remained arguably the strongest defender of this Jacobin ideal in France. In this article, I will discuss the role of France’s restrictive language policy through the prism of the CC’s jurisprudence. Overall, I will argue that the CC made reference to the (Jacobin) state-nation concept, a concept that is discussed in the first part of the paper, in order to fight the revival of regional languages in France over recent decades. The clause making French the official language in 1992 was functional to this policy. The intriguing aspect is that in France the CC managed to standardise France’s policy vis-à-vis regional and minority languages through its jurisprudence; an issue discussed in the second part of the paper. But in those regions with a stronger tradition of identity, particularly in the French overseas territories, the third part of the paper argues, normative reality has increasingly become under pressure. Therefore, a discrepancy between the ‘law in courts’ and the compliance with these decisions (‘law in action’) has been emerging over recent years. Amid some signs of opening of France to minorities, this contradiction delineates a trend that might well continue in future.

The Fundamental Rights Agency and Civil Society: Reminding the Gardeners of their Plants' Roots. EDAP 02/2012, 2012

The European Union Agency for Fundamental Rights (FRA), the EU body responsible for advising EU institutions on fundamental rights, is equipped with a Fundamental Rights Platform (FRP) to ensure an on-going and structured exchange of information and feedback between the FRA and Civil Society. When the FRA was founded in 2007, there was little pre-existing knowledge on how to design such a Platform; hence, the development of the relationship between the FRA and Civil Society over the first five years proved an interesting experiment. Although the Platform was never intended as a mechanism of democratic co-decision making, it is far more than a loose marketplace where Civil Society actors across the spectrum of fundamental rights themes gather. The Platform offers channels of consultation and exchange not only among the participants but also with the FRA. It allows for cross-pollination, ensuring informed grassroots input into FRA work and FRA expertise flow to Civil Society actors. This synergetic relationship builds upon both the self-organising forces of Civil Society and the terms of references of the FRP as defined by the FRA. The Platform allows to find a certain unity in the remarkable diversity of fundamental rights voices. To what degree, however, the Platform’s dynamics allow the transformation of sometimes ‘compartmentalised’ single human rights discussions into wider trans-sectoral and transnational debates within the Human Rights Community depends on the motivation and the interest(s) of the different Civil Society players.

Running Climate Models On The NERC Cluster Grid Using G-Rex

Compute grids are used widely in many areas of environmental science, but there has been limited uptake of grid computing by the climate modelling community, partly because the characteristics of many climate models make them difficult to use with popular grid middleware systems. In particular, climate models usually produce large volumes of output data, and running them usually involves complicated workflows implemented as shell scripts. For example, NEMO (Smith et al. 2008) is a state-of-the-art ocean model that is used currently for operational ocean forecasting in France, and will soon be used in the UK for both ocean forecasting and climate modelling. On a typical modern cluster, a particular one year global ocean simulation at 1-degree resolution takes about three hours when running on 40 processors, and produces roughly 20 GB of output as 50000 separate files. 50-year simulations are common, during which the model is resubmitted as a new job after each year. Running NEMO relies on a set of complicated shell scripts and command utilities for data pre-processing and post-processing prior to job resubmission. Grid Remote Execution (G-Rex) is a pure Java grid middleware system that allows scientific applications to be deployed as Web services on remote computer systems, and then launched and controlled as if they are running on the user's own computer. Although G-Rex is general purpose middleware it has two key features that make it particularly suitable for remote execution of climate models: (1) Output from the model is transferred back to the user while the run is in progress to prevent it from accumulating on the remote system and to allow the user to monitor the model; (2) The client component is a command-line program that can easily be incorporated into existing model work-flow scripts. G-Rex has a REST (Fielding, 2000) architectural style, which allows client programs to be very simple and lightweight and allows users to interact with model runs using only a basic HTTP client (such as a Web browser or the curl utility) if they wish. This design also allows for new client interfaces to be developed in other programming languages with relatively little effort. The G-Rex server is a standard Web application that runs inside a servlet container such as Apache Tomcat and is therefore easy to install and maintain by system administrators. G-Rex is employed as the middleware for the NERC1 Cluster Grid, a small grid of HPC2 clusters belonging to collaborating NERC research institutes. Currently the NEMO (Smith et al. 2008) and POLCOMS (Holt et al, 2008) ocean models are installed, and there are plans to install the Hadley Centre’s HadCM3 model for use in the decadal climate prediction project GCEP (Haines et al., 2008). The science projects involving NEMO on the Grid have a particular focus on data assimilation (Smith et al. 2008), a technique that involves constraining model simulations with observations. The POLCOMS model will play an important part in the GCOMS project (Holt et al, 2008), which aims to simulate the world’s coastal oceans. A typical use of G-Rex by a scientist to run a climate model on the NERC Cluster Grid proceeds as follows :(1) The scientist prepares input files on his or her local machine. (2) Using information provided by the Grid’s Ganglia3 monitoring system, the scientist selects an appropriate compute resource. (3) The scientist runs the relevant workflow script on his or her local machine. This is unmodified except that calls to run the model (e.g. with “mpirun”) are simply replaced with calls to "GRexRun" (4) The G-Rex middleware automatically handles the uploading of input files to the remote resource, and the downloading of output files back to the user, including their deletion from the remote system, during the run. (5) The scientist monitors the output files, using familiar analysis and visualization tools on his or her own local machine. G-Rex is well suited to climate modelling because it addresses many of the middleware usability issues that have led to limited uptake of grid computing by climate scientists. It is a lightweight, low-impact and easy-to-install solution that is currently designed for use in relatively small grids such as the NERC Cluster Grid. A current topic of research is the use of G-Rex as an easy-to-use front-end to larger-scale Grid resources such as the UK National Grid service.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

Distributed mining of molecular fragments

In real world applications sequential algorithms of data mining and data exploration are often unsuitable for datasets with enormous size, high-dimensionality and complex data structure. Grid computing promises unprecedented opportunities for unlimited computing and storage resources. In this context there is the necessity to develop high performance distributed data mining algorithms. However, the computational complexity of the problem and the large amount of data to be explored often make the design of large scale applications particularly challenging. In this paper we present the first distributed formulation of a frequent subgraph mining algorithm for discriminative fragments of molecular compounds. Two distributed approaches have been developed and compared on the well known National Cancer Institute’s HIV-screening dataset. We present experimental results on a small-scale computing environment.

High performance subgraph mining in molecular compounds

Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.