A new and finite family of solutions of hydrodynamics. Part I: Fits to pseudorapidity distributions

We highlight some of the interesting properties of a new and finite, exact family of solutions of 1 + 1 dimensional perfect fluid relativistic hydrodynamics. After reviewing the main properties of this family of solutions, we present the formulas that connect it to the measured rapidity and pseudo-rapidity densities and illustrate the results with fits to p+p collisions at 8 TeV and Pb+Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV.Comment: Invited talk of T. Csorgo at the WPCF 2018 conference in Cracow, Poland, May 22-26, 2018. Submitted to Acta Physica Polonica

Distributed feature selection for efficient economic big data analysis

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although such data offers super opportunities for economic analysis, its low-quality, high-dimensionality and huge-volume pose great challenges on efficient analysis of economic big data. The existing methods have primarily analyzed economic data from the perspective of econometrics, which involves limited indicators and demands prior knowledge of economists. When embracing large varieties of economic factors, these methods tend to yield unsatisfactory performance. To address the challenges, this paper presents a new framework for efficient analysis of high-dimensional economic big data based on innovative distributed feature selection. Specifically, the framework combines the methods of economic feature selection and econometric model construction to reveal the hidden patterns for economic development. The functionality rests on three pillars: (i) novel data pre-processing techniques to prepare high-quality economic data, (ii) an innovative distributed feature identification solution to locate important and representative economic indicators from multidimensional data sets, and (iii) new econometric models to capture the hidden patterns for economic development. The experimental results on the economic data collected in Dalian, China, demonstrate that our proposed framework and methods have superior performance in analyzing enormous economic data.This work is supported by National Natural Science Foundation Project of China (U1301253), Science and Technology Planning Key Project of Guangdong Province, China (2015B010110006) and Research Office of Dalian Government in China