Observation of the decay mode K_L -> pi^+ pi^- e^+ e^-

We report on results of an experimental search for the K_L -> pi^+ pi^- e^+ e^- decay mode. We found 13.5 +- 4.0 events and determined its branching ratio to be (4.4 +- 1.3(stat) +- 0.5(syst))*10^{-7}. The result agrees well with the theoretical prediction.Comment: 9 pages, 6 eps-figures, LaTeX2e, graphicx package, submitted to Physics Letters

Unusual Low-Temperature Phase in VO2_2 Nanoparticles

We present a systematic investigation of the crystal and electronic structure and the magnetic properties above and below the metal-insulator transition of ball-milled VO$_2$ nanoparticles and VO$_2$ microparticles. For this research, we performed a Rietveld analysis of synchrotron radiation x-ray diffraction data, O $K$ x-ray absorption spectroscopy, V $L_3$ resonant inelastic x-ray scattering, and magnetic susceptibility measurements. This study reveals an unusual low-temperature phase that involves the formation of an elongated and less-tilted V-V pair, a narrowed energy gap, and an induced paramagnetic contribution from the nanoparticles. We show that the change in the crystal structure is consistent with the change in the electronic states around the Fermi level, which leads us to suggest that the Peierls mechanism contributes to the energy splitting of the $a_{1g}$ state. Furthermore, we find that the high-temperature rutile structure of the nanoparticles is almost identical to that of the microparticles.Comment: 7 pages, 8 figures, 2 table

Experimental search for the decay mode K_L -> pi^0 gamma e^+ e^-

We report on results of a search for the decay mode K_L -> pi^0 gamma e^+ e^- conducted by the E162 experiment at KEK. We observed no events and set a 90% confidence level upper limit of Br(K_L -> pi^0 gamma e^+ e^-)< 7.1x10^{-7} for its branching ratio. This is the first published experimental result on this decay mode.Comment: 10 pages, 4 figures, submitted to Physics Letters

An efficient algorithm for learning with semi-bandit feedback

We consider the problem of online combinatorial optimization under semi-bandit feedback. The goal of the learner is to sequentially select its actions from a combinatorial decision set so as to minimize its cumulative loss. We propose a learning algorithm for this problem based on combining the Follow-the-Perturbed-Leader (FPL) prediction method with a novel loss estimation procedure called Geometric Resampling (GR). Contrary to previous solutions, the resulting algorithm can be efficiently implemented for any decision set where efficient offline combinatorial optimization is possible at all. Assuming that the elements of the decision set can be described with d-dimensional binary vectors with at most m non-zero entries, we show that the expected regret of our algorithm after T rounds is O(m sqrt(dT log d)). As a side result, we also improve the best known regret bounds for FPL in the full information setting to O(m^(3/2) sqrt(T log d)), gaining a factor of sqrt(d/m) over previous bounds for this algorithm.Comment: submitted to ALT 201