Strong Converse Exponent for Entanglement-Assisted Communication

We determine the exact strong converse exponent for entanglement-assisted classical communication of a quantum channel. Our main contribution is the derivation of an upper bound for the strong converse exponent which is characterized by the sandwiched R\'enyi divergence. It turns out that this upper bound coincides with the lower bound of Gupta and Wilde (Commun Math Phys 334:867--887, 2015). Thus, the strong converse exponent follows from the combination of these two bounds. Our result has two implications. Firstly, it implies that the exponential bound for the strong converse property of quantum-feedback-assisted classical communication, derived by Cooney, Mosonyi and Wilde (Commun Math Phys 344:797--829, 2016), is optimal. This answers their open question in the affirmative. Hence, we have determined the exact strong converse exponent for this problem as well. Secondly, due to an observation of Leung and Matthews, it can be easily extended to deal with the transmission of quantum information under the assistance of entanglement or quantum feedback, yielding similar results. The above findings provide, for the first time, a complete operational interpretation to the channel's sandwiched R\'enyi information of order $\alpha > 1$.Comment: V2: minor changes, presentation improve

A Study on High-Speed Rail Pricing Strategy in the Context of Modes Competition

High-speed rail (HSR) has developed rapidly in China over the recent years, for the less pollution, faster speed, comfort, and safety. However, there is still an issue on how to improve the seat occupancy rates for some HSR lines. This research analyzes the pricing strategy for HSR in Wuhan-Guangzhou corridor based on the competition among different transport modes with the aim of improving occupancy rates. It starts with the theoretical analysis of relationship between market share and ticket fare, and then disaggregate choice models with nested structure based on stated preference (SP) data are established to obtain the market share of HSR under specific ticket fare. Finally, a pricing strategy is proposed to improve the occupancy rates for Wuhan-Guangzhou HSR. The results confirm that a pricing strategy with floating fare should be accepted to improve the profit of HSR; to be specific, the ticket fare should be set in lower level on weekdays and higher level on holidays

Interlayer charge transfer in ReS 2 / WS 2 van der Waals heterostructures

We observed ultrafast charge transfer between distorted 1T−ReS2 with anisotropic in-plane electronic and optical properties and 2H−WS2 that is in-plane isotropic. Heterostructures of monolayer ReS2/monolayer WS2 and bilayer ReS2/monolayer WS2 were fabricated by mechanical exfoliation and dry transfer techniques. Significant photoluminescence quenching of WS2 in the heterostructures indicates efficient charge transfer. In femtosecond transient absorption measurements, it was found that holes injected in monolayer or bilayer ReS2 transfer to WS2 on a timescale that is shorter than the time resolution of the measurement. This observation provides evidence that the holes are delocalized in bilayer ReS2, revealing strong van der Waals interlayer couplings. These results also show that ReS2 and WS2 form type-II heterostructures with excellent charge transfer properties

A snoRNA modulates mRNA 3' end processing and regulates the expression of a subset of mRNAs.

mRNA 3' end processing is an essential step in gene expression. It is well established that canonical eukaryotic pre-mRNA 3' processing is carried out within a macromolecular machinery consisting of dozens of trans-acting proteins. However, it is unknown whether RNAs play any role in this process. Unexpectedly, we found that a subset of small nucleolar RNAs (snoRNAs) are associated with the mammalian mRNA 3' processing complex. These snoRNAs primarily interact with Fip1, a component of cleavage and polyadenylation specificity factor (CPSF). We have functionally characterized one of these snoRNAs and our results demonstrated that the U/A-rich SNORD50A inhibits mRNA 3' processing by blocking the Fip1-poly(A) site (PAS) interaction. Consistently, SNORD50A depletion altered the Fip1-RNA interaction landscape and changed the alternative polyadenylation (APA) profiles and/or transcript levels of a subset of genes. Taken together, our data revealed a novel function for snoRNAs and provided the first evidence that non-coding RNAs may play an important role in regulating mRNA 3' processing

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy