Nonlinear plasmonics at high temperatures

We solve the Maxwell and heat equations self-consistently for metal nanoparticles under intense continuous wave (CW) illumination. Unlike previous studies, we rely on {\em experimentally}-measured data for the metal permittivity for increasing temperature and for the visible spectral range. We show that the thermal nonlinearity of the metal can lead to substantial deviations from the predictions of the linear model for the temperature and field distribution, and thus, can explain qualitatively the strong nonlinear scattering from such configurations observed experimentally. We also show that the incompleteness of existing data of the temperature dependence of the thermal properties of the system prevents reaching a quantitative agreement between the measured and calculated scattering data. This modelling approach is essential for the identification of the underlying physical mechanism responsible for the thermo-optical nonlinearity of the metal and should be adopted in all applications of high temperature nonlinear plasmonics, especially for refractory metals, both for CW and pulsed illumination

Constrain intergalactic medium from the SZ effect map

In this paper, we try to detect the SZ effect in the 2MASS DWT clusters and less bound objects in order to constrain the warm-hot intergalactic medium distribution on large scales by cross-correlation analysis. The results of both observed WMAP and mock SZ effect map indicate that the hot gas distributes from inside as well as outside of the high density regions of galaxy clusters, which is consistent with the results of both observation and hydro simulation. Therefore, the DWT measurement of the cross-correlation would be a powerful tool to probe the missing of baryons in the Universe.Comment: 9 pages,2 figures. Accepted for publication in Mod. Phys. Lett.

Probing dipole-forbidden autoionizing states by isolated attosecond pulses

We propose a general technique to retrieve the information of dipole-forbidden resonances in the autoionizing region. In the simulation, a helium atom is pumped by an isolated attosecond pulse in the extreme ultraviolet (EUV) combined with a few-femtosecond laser pulse. The excited wave packet consists of the $^1S$, $^1P$, and $^1D$ states, including the background continua, near the $2s2p(^1P)$ doubly excited state. The resultant electron spectra with various laser intensities and time delays between the EUV and laser pulses are obtained by a multilevel model and an ab initio time-dependent Schr\"odinger equation calculation. By taking the ab initio calculation as a "virtual measurement", the dipole-forbidden resonances are characterized by the multilevel model. We found that in contrast to the common assumption, the nonresonant coupling between the continua plays a significant role in the time-delayed electron spectra, which shows the correlation effect between photoelectrons before they leave the core. This technique takes the advantages of ultrashort pulses uniquely and would be a timely test for the current attosecond technology.Comment: 10 pages, 6 figure

Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms

Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. \emph{Offline} evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature. Common practice is to create a simulator which simulates the online environment for the problem at hand and then run an algorithm against this simulator. However, creating simulator itself is often difficult and modeling bias is usually unavoidably introduced. In this paper, we introduce a \emph{replay} methodology for contextual bandit algorithm evaluation. Different from simulator-based approaches, our method is completely data-driven and very easy to adapt to different applications. More importantly, our method can provide provably unbiased evaluations. Our empirical results on a large-scale news article recommendation dataset collected from Yahoo! Front Page conform well with our theoretical results. Furthermore, comparisons between our offline replay and online bucket evaluation of several contextual bandit algorithms show accuracy and effectiveness of our offline evaluation method.Comment: 10 pages, 7 figures, revised from the published version at the WSDM 2011 conferenc