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

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

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

Learning what matters - Sampling interesting patterns

In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into account-but not both. Consequently, the analyst has to invest substantial effort in identifying those patterns that are relevant to her specific interests and goals. To address this problem, we propose a novel approach that combines pattern sampling with interactive data mining. In particular, we introduce the LetSIP algorithm, which builds upon recent advances in 1) weighted sampling in SAT and 2) learning to rank in interactive pattern mining. Specifically, it exploits user feedback to directly learn the parameters of the sampling distribution that represents the user's interests. We compare the performance of the proposed algorithm to the state-of-the-art in interactive pattern mining by emulating the interests of a user. The resulting system allows efficient and interleaved learning and sampling, thus user-specific anytime data exploration. Finally, LetSIP demonstrates favourable trade-offs concerning both quality-diversity and exploitation-exploration when compared to existing methods.Comment: PAKDD 2017, extended versio