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  1. Properties of the nuclear equation of state (EoS) can be probed by measuring the dynamical properties of nucleus-nucleus collisions. In this study, we present the directed flow (v1), elliptic flow (v2) and stopping (VarXZ) measured in fixed target Sn+ Sn collisions at 270AMeV with the S'll'RlT Time Projection Chamber. We perform Bayesian analyses in which EoS parameters are var­ied simultaneously within the Improved Quantum Molecular Dynamics-Skyrme (ImQMD-Sky) transport code to obtain a multivariate correlated constraint. The varied parameters include symmetry energy, S0, and slope of the symme­try energy, L, at saturation density, isoscalar effective mass, m;/mN, isovector effective mass, m􀀒/mN and the in-medium cross-section enhancement factor rJ. We find that the flow and VarXZ observables are sensitive to the splitting of proton and neutron effective masses and the in-medium cross-section. Compar­isons of ImQMD-Sky predictions to the S'll' RJT data suggest a narrow range of preferred values for m;/mN, m􀀕/mN and 1/· 
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    Free, publicly-accessible full text available June 1, 2025
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  8. Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples and extremely high-dimensional features, solving sparse SVMs remains challenging. By noting that sparse SVMs induce sparsities in both feature and sample spaces, we propose a novel approach, which is based on accurate estimations of the primal and dual optima of sparse SVMs, to simultaneously identify the features and samples that are guaranteed to be irrelevant to the outputs. Thus, we can remove the identified inactive samples and features from the training phase, leading to substantial savings in both the memory usage and computational cost without sacrificing accuracy. To the best of our knowledge, the proposed method is the first static feature and sample reduction method for sparse SVMs. Experiments on both synthetic and real datasets (e.g., the kddb dataset with about 20 million samples and 30 million features) demonstrate that our approach significantly outperforms state-of-the-art methods and the speedup gained by our approach can be orders of magnitude. 
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