National Research University – Higher School of Economics

About: National Research University – Higher School of Economics is a education organization based out in Moscow, Russia. It is known for research contribution in the topics: Population & Politics. The organization has 12873 authors who have published 23376 publications receiving 256396 citations.

Monopsony in Labor Markets: A Meta-Analysis:

Abstract: When jobs offered by different employers are not perfect substitutes, employers gain wage-setting power; the extent of this power can be captured by the elasticity of labor supply to the firm. The ...

First measurement of the $CP$-violating phase $\phi_s^{d\overline{d}}$ in $B_s^0\to(K^+\pi^-)(K^-\pi^+)$ decays

Abstract: A flavour-tagged decay-time-dependent amplitude analysis of B$_{s}^{0}$ → (K$^{+}$π$^{−}$)(K$^{−}$π$^{+}$) decays is presented in the K$^{±}$π$^{∓}$ mass range from 750 to 1600MeV/c$^{2}$. The analysis uses pp collision data collected with the LHCb detector at centre-of-mass energies of 7 and 8 TeV, corresponding to an integrated luminosity of 3.0 fb$^{−1}$. Several quasi-two-body decay modes are considered, corresponding to K$^{±}$π$^{∓}$ combinations with spin 0, 1 and 2, which are dominated by the K$_{0}^{*}$ (800)$^{0}$ and K$_{0}^{*}$ (1430)$^{0}$, the K$^{*}$(892)$^{0}$ and the K$_{2}^{*}$ (1430)$^{0}$ resonances, respectively. The longitudinal polarisation fraction for the $ {B}_s^0\to {K}^{\ast }(892){}^{\circ}{\overline{K}}^{\ast }{(892)}^0 $ decay is measured as f$_{L}$ = 0.208 ± 0.032 ± 0.046, where the first uncertainty is statistical and the second is systematic. The first measurement of the mixing-induced CP-violating phase, $ {\phi}_s^{d\overline{d}} $ , in $ b\to d\overline{d}s $ transitions is performed, yielding a value of $ {\phi}_s^{d\overline{d}}=-0.10\pm 0.13\left(\mathrm{stat}\right)\pm 0.14 $ (syst) rad.

Robust Data Mining

Abstract: Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field ofrobust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. Thisbrief will appeal to theoreticians and data miners working in this field.