Flavor and CP Violation with Fourth Generations Revisited

The Standard Model predicts a very small CP violation phase $\sin2\Phi^{\rm SM}_{B_s} \simeq -0.04$%= \arg M_{12} \simeq \arg\,(V^*_{ts}V_{tb})^2$in$B_s$--$\bar B_s$mixing.$\lambda^2\eta$. Any finite value of$\Phi_{B_s}$measured at the Tevatron would imply New Physics. With recent hints for finite$\sin2\Phi_{B_s}$, experiments at the Tevatron, we reconsider the possibility of a 4th generation. As recent direct search bounds have become considerably heavier than 300 GeV, we take the$t'$mass to be near the unitarity bound of 500 GeV. Combining the measured values of$\Delta m_{B_s}$with${\cal B}(B \to X_s\ell^+\ell^-)$, together with typical$f_{B_s}$values, we find a sizable$\sin2\Phi^{\rm SM4}_{B_s} \sim -0.33$. Using$m_{b'} = 480$GeV, we extract the range$0.06 < |V_{t'b}| < 0.13$from the constraints of$\Gamma(Z\to b\bar b)$,$\Delta m_{D}$and${\cal B}(K^+\to\pi^+\nu\bar\nu)$. A future measurement of${\cal B}(K_L\to\pi^0\nu\bar\nu)$will determine$V_{t'd}$.Comment: 8 pages, 11 figure

Data-Driven Multi-step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network

Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people's activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 minutes. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps

Symbol-Level Selective Full-Duplex Relaying with Power and Location Optimization

In this paper, a symbol-level selective transmission for full-duplex (FD) relaying networks is proposed to mitigate error propagation effects and improve system spectral efficiency. The idea is to allow the FD relay node to predict the correctly decoded symbols of each frame, based on the generalized square deviation method, and discard the erroneously decoded symbols, resulting in fewer errors being forwarded to the destination node. Using the capability for simultaneous transmission and reception at the FD relay node, our proposed strategy can improve the transmission efficiency without extra cost of signalling overhead. In addition, targeting on the derived expression for outage probability, we compare it with half-duplex (HD) relaying case, and provide the transmission power and relay location optimization strategy to further enhance system performance. The results show that our proposed scheme outperforms the classic relaying protocols, such as cyclic redundancy check based selective decode-and-forward (S-DF) relaying and threshold based S-DF relaying in terms of outage probability and bit-error-rate. Moreover, the performances with optimal power allocation is better than that with equal power allocation, especially when the FD relay node encounters strong self-interference and/or it is close to the destination node.Comment: 34 pages (single-column), 14 figures, 2 tables, accepted pape