14,412 research outputs found
Flavor and CP Violation with Fourth Generations Revisited
The Standard Model predicts a very small CP violation phase %= \arg M_{12} \simeq \arg\,(V^*_{ts}V_{tb})^2B_s\bar B_s\lambda^2\eta\Phi_{B_s}\sin2\Phi_{B_s}t'\Delta m_{B_s}{\cal B}(B \to X_s\ell^+\ell^-)f_{B_s}\sin2\Phi^{\rm
SM4}_{B_s} \sim -0.33m_{b'} = 4800.06 < |V_{t'b}| < 0.13\Gamma(Z\to b\bar b)\Delta m_{D}{\cal
B}(K^+\to\pi^+\nu\bar\nu){\cal
B}(K_L\to\pi^0\nu\bar\nu)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
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