Photon scattering by an atomic ensemble coupled to a one-dimensional nanophotonic waveguide

We theoretically investigate the quantum scattering of a single-photon pulse interacting with an ensemble of $\Lambda$-type three-level atoms coupled to a one-dimensional waveguide. With an effective non-Hermitian Hamiltonian, we study the collective interaction between the atoms mediated by the waveguide mode. In our scheme, the atoms are randomly placed in the lattice along the axis of the one-dimensional waveguide, which closely corresponds to the practical condition that the atomic positions can not be controlled precisely in experiment. Many interesting optical properties occur in our waveguide-atom system, such as electromagnetically induced transparency (EIT) and optical depth. Moreover, we observe that strong photon-photon correlation with quantum beats can be generated in the off-resonant case, which provides an effective candidate for producing non-classical light in experiment. With remarkable progress in waveguide-emitter system, our scheme may be feasible in the near future.Comment: 10 pages,7 figure

Valley-dependent gauge fields for ultracold atoms in square optical superlattices

We propose an experimental scheme to realize the valley-dependent gauge fields for ultracold fermionic atoms trapped in a state-dependent square optical lattice. Our scheme relies on two sets of Raman laser beams to engineer the hopping between adjacent sites populated by two-component fermionic atoms. One set of Raman beams are used to realize a staggered \pi-flux lattice, where low energy atoms near two inequivalent Dirac points should be described by the Dirac equation for spin-1/2 particles. Another set of laser beams with proper Rabi frequencies are added to further modulate the atomic hopping parameters. The hopping modulation will give rise to effective gauge potentials with opposite signs near the two valleys, mimicking the interesting strain-induced pseudo-gauge fields in graphene. The proposed valley-dependent gauge fields are tunable and provide a new route to realize quantum valley Hall effects and atomic valleytronics.Comment: 5+ pages, 2 figures; language polished, references and discussions added; accepted by PR

Echocardiographic parameters versus CHA2DS2-VASc score in prediction of overall cardiac events, heart failure, and stroke in non-valvular atrial fibrillation

     Background: Apart from stroke, atrial fibrillation (AF) is associated with higher mortality and heart failure (HF), in which risk stratification scheme is lacking. Therefore this investigation examined the prognostic value of echocardiographic predictors against CHA2DS2-VASc score in permanent non- -valvular AF (NVAF). Methods: In 252 asymptomatic or mildly symptomatic consecutive patients with NVAF, comprehensive echocardiography was performed. Left atrial deformation parameters were also obtained by two-dimen­sional speckle tracking echocardiography. End-points pertaining to HF deterioration, ischemic stroke and cardiac death were recorded. Results: There were 74 cardiovascular events, including 44 deterioration of HF, 22 ischemic strokes and 8 cardiovascular deaths during an average follow-up period of 20.8 ± 13.5 months (interquartile range, 8–31 months). For prediction of overall prognosis and HF, left ventricular mass index, peak early filling velocity (E), and E to tissue Doppler mitral annular early diastolic velocity ratio (E/e’) outper­formed CHA2DS2-VASc score in multivariate analysis, area under curve, and stepwise nested regression models. Left ventricular hypertrophy and E/e’ > 8 showed worse overall and heart-failure free survival in Kaplan-Meier curves. For prediction of ischemic stroke, the addition of E or E/e’ to CHA2DS2-VASc score provides extra prognostic value. Conclusions: Echocardiographic parameters offer incremental value over CHA2DS2-VASc score for prediction of future cardiac events in NVAF. (Cardiol J 2018; 25, 1: 60–71

Short-Term Truckload Spot Rates\u27 Prediction in Consideration of Temporal and Between-Route Correlations

Truckload spot rate (TSR), defined as a price offered on the spot to transport a certain cargo by using an entire truck on a target transportation line, usually price per kilometer-ton, is a key factor in shaping the freight market. In particular, the prediction of short-term TSR is of great importance to the daily operations of the trucking industry. However, existing predictive practices have been limited largely by the availability of multilateral information, such as detailed intraday TSR information. Fortunately, the emerging online freight exchange (OFEX) platforms provide unique opportunities to access and fuse more data for probing the trucking industry. As such, this paper aims to leverage the high-resolution trucking data from an OFEX platform to forecast short-term TSR. Specifically, a lagged coefficient weighted matrix-based multiple linear regression modeling (Lag-WMR) is proposed, and exogenous variables are selected by the light gradient boosting (LGB) method. This model simultaneously incorporates the dependency between historical and current TSR (temporal correlation) and correlations between the rates on alternative routes (between-route correlation). In addition, the effects of incorporating temporal and between-route correlations, time-lagged correlation and exogenous variable selection in modeling are emphasized and assessed through a case study on short-term TSR in Southwest China. The comparative results show that the proposed Lag-WMR model outperforms autoregressive integrated moving average (ARIMA) model and LGB in terms of model fitting and the quality and stability of predictions. Further research could focus on rates\u27 standardization, to define a practical freight index for the trucking industry. Although our results are specific to the Chinese trucking market, the method of analysis serves as a general model for similar international studies