Imagining the Great Lakes Region: discourses and practices of civil society regional approaches for peacebuilding in Rwanda, Burundi and DR Congo

The idea has gained ground in recent years that, as conflicts in the countries of the Great Lakes Region are strongly interlinked, regional approaches are necessary to resolve them. This interest in regional dimensions of conflict and peacebuilding also gains currency in other parts of the world. Attention to regional approaches is reflected in the efforts of international organisations and donors to promote civil society peacebuilding. They assume that regional cooperation and exchange between civil society organisations contribute to peace, and provide an alternative to single-country interventions or regional diplomatic initiatives. This paper explores how such assumptions work out in practice. Experiences in the Great Lakes Region show that local and international organisations have difficulty in analysing the regional character of conflict and arriving at collaborative regional strategies. Moreover, local civil society organisations are deeply embedded in the politics of regional conflict. Consequently, the shift to regional peacebuilding approaches remains more theoretical than practical. This paper suggests that international supporting organisations need to adjust their ambitions in regional peacebuilding, but nonetheless have roles in fostering regional identification among civil society organisations

Energy Dependence of Particle Production in nucleus-nucleus collisions at the CERN SPS

New preliminary results on kaon and pion production in central 30AGeV Pb+Pb collisions are presented. The data are compared to data at lower and higher energies to examine the energy dependence of the kaon to pion ratios and the inverse slope parameters of kaons. The results are compared to expectations from models with and without a phase transition to the Quark Gluon Plasma.Comment: 4 pages, 5 figures, presented at XXXVIIIth Rencontres de Moriond, QCD and High Energy Hadronic Interactions sessio

Comparing energy loss phenomenology

High-pT particle production is suppressed in heavy ion collisions due to parton energy loss in dense QCD matter. Here we present a systematic comparison of two different theoretical approximations to parton energy loss calculations: the opacity expansion and the multiple-soft scattering approximation for the simple case of a quark traversing a homogeneous piece of matter with fixed length (the TECHQM 'brick problem'), with focus on the range of parameters that is relevant for interpreting RHIC measurements of high-pT hadron suppression.Comment: Proceedings for workshop 'High-pt at the LHC 09', Prague, submitted to Proceedings of Science. 8 pages, 3 figures Update v2: fix typos in Eq 1.

Bulk Viscosity of Interacting Hadrons

We show that first approximations to the bulk viscosity $\eta_v$ are expressible in terms of factors that depend on the sound speed $v_s$, the enthalpy, and the interaction (elastic and inelastic) cross section. The explicit dependence of $\eta_v$ on the factor $(\frac 13 - v_s^2)$ is demonstrated in the Chapman-Enskog approximation as well as the variational and relaxation time approaches. The interesting feature of bulk viscosity is that the dominant contributions at a given temperature arise from particles which are neither extremely nonrelativistic nor extremely relativistic. Numerical results for a model binary mixture are reported.Comment: 4 pages, 1 figure, Contribution to Quark Matter 2009, Knoxville, Tennessee, US

Skating on slippery ice

The friction of a stationary moving skate on smooth ice is investigated, in particular in relation to the formation of a thin layer of water between skate and ice. It is found that the combination of ploughing and sliding gives a friction force that is rather insensitive for parameters such as velocity and temperature. The weak dependence originates from the pressure adjustment inside the water layer. For instance, high velocities, which would give rise to high friction, also lead to large pressures, which, in turn, decrease the contact zone and so lower the friction. The theory is a combination and completion of two existing but conflicting theories on the formation of the water layer.Comment: 26 pages, 8 figures Posted at SciPos

Reptation in the Rubinstein-Duke model: the influence of end-reptons dynamics

We investigate the Rubinstein-Duke model for polymer reptation by means of density-matrix renormalization group techniques both in absence and presence of a driving field. In the former case the renewal time \tau and the diffusion coefficient D are calculated for chains up to N=150 reptons and their scaling behavior in N is analyzed. Both quantities scale as powers of N: $\tau \sim N^z$ and $D \sim 1/N^x$ with the asymptotic exponents z=3 and x=2, in agreement with the reptation theory. For an intermediate range of lengths, however, the data are well-fitted by some effective exponents whose values are quite sensitive to the dynamics of the end reptons. We find 2.7 <z< 3.3 and 1.8 <x< 2.1 for the range of parameters considered and we suggest how to influence the end reptons dynamics in order to bring out such a behavior. At finite and not too small driving field, we observe the onset of the so-called band inversion phenomenon according to which long polymers migrate faster than shorter ones as opposed to the small field dynamics. For chains in the range of 20 reptons we present detailed shapes of the reptating chain as function of the driving field and the end repton dynamics.Comment: RevTeX 12 Pages and 14 figure

Interpretable multiclass classification by MDL-based rule lists

Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts. Examples of interpretable classification models include decision trees, rule sets, and rule lists. Learning such models often involves optimizing hyperparameters, which typically requires substantial amounts of data and may result in relatively large models. In this paper, we consider the problem of learning compact yet accurate probabilistic rule lists for multiclass classification. Specifically, we propose a novel formalization based on probabilistic rule lists and the minimum description length (MDL) principle. This results in virtually parameter-free model selection that naturally allows to trade-off model complexity with goodness of fit, by which overfitting and the need for hyperparameter tuning are effectively avoided. Finally, we introduce the Classy algorithm, which greedily finds rule lists according to the proposed criterion. We empirically demonstrate that Classy selects small probabilistic rule lists that outperform state-of-the-art classifiers when it comes to the combination of predictive performance and interpretability. We show that Classy is insensitive to its only parameter, i.e., the candidate set, and that compression on the training set correlates with classification performance, validating our MDL-based selection criterion