Numerical simulation of the optimal two-mode attacks for two-way continuous-variable quantum cryptography in reverse reconciliation

We analyze the security of the two-way continuous-variable quantum key distribution protocol in reverse reconciliation against general two-mode attacks, which represent all accessible attacks at fixed channel parameters. Rather than against one specific attack model, the expression of secret key rates of the two-way protocol are derived against all accessible attack models. It is found that there is an optimal two-mode attack to minimize the performance of the protocol in terms of both secret key rates and maximal transmission distances. We identify the optimal two-mode attack, give the specific attack model of the optimal two-mode attack and show the performance of the two-way protocol against the optimal two-mode attack. Even under the optimal two-mode attack, the performances of two-way protocol are still better than the corresponding one-way protocol, which shows the advantage of making a double use of the quantum channel and the potential of long-distance secure communication using two-way protocol.Comment: 14 pages, 8 figure

Improvement of two-way continuous-variable quantum key distribution with virtual photon subtraction

We propose a method to improve the performance of two-way continuous-variable quantum key distribution protocol by virtual photon subtraction. The Virtual photon subtraction implemented via non-Gaussian post-selection not only enhances the entanglement of two-mode squeezed vacuum state but also has advantages in simplifying physical operation and promoting efficiency. In two-way protocol, virtual photon subtraction could be applied on two sources independently. Numerical simulations show that the optimal performance of renovated two-way protocol is obtained with photon subtraction only used by Alice. The transmission distance and tolerable excess noise are improved by using the virtual photon subtraction with appropriate parameters. Moreover, the tolerable excess noise maintains a high value with the increase of distance so that the robustness of two-way continuous-variable quantum key distribution system is significantly improved, especially at long transmission distance.Comment: 15 pages, 6 figure

Finite-size analysis of continuous-variable measurement-device-independent quantum key distribution

We study the impact of the finite-size effect on the continuous-variable measurement-device-independent quantum key distribution (CV-MDI QKD) protocol, mainly considering the finite-size effect on the parameter estimation procedure. The central-limit theorem and maximum likelihood estimation theorem are used to estimate the parameters. We also analyze the relationship between the number of exchanged signals and the optimal modulation variance in the protocol. It is proved that when Charlie's position is close to Bob, the CV-MDI QKD protocol has the farthest transmission distance in the finite-size scenario. Finally, we discuss the impact of finite-size effects related to the practical detection in the CV-MDI QKD protocol. The overall results indicate that the finite-size effect has a great influence on the secret key rate of the CV-MDI QKD protocol and should not be ignored.Comment: 9 pages, 9 figure

A New Method for Impeller Inlet Design of Supercritical CO2 Centrifugal Compressors in Brayton Cycles

Supercritical Carbon Dioxide (SCO2) is considered as a potential working fluid in next generation power and energy systems. The SCO2\ua0Brayton cycle is advantaged with higher cycle efficiency, smaller compression work, and more compact layout, as compared with traditional cycles. When the inlet total condition of the compressor approaches the critical point of the working fluid, the cycle efficiency is further enhanced. However, the flow acceleration near the impeller inducer causes the fluid to enter two-phase region, which may lead to additional aerodynamic losses and flow instability. In this study, a new impeller inlet design method is proposed to achieve a better balance among the cycle efficiency, compressor compactness, and inducer condensation. This approach couples a concept of the maximum swallowing capacity of real gas and a new principle for condensation design. Firstly, the mass flow function of real gas centrifugal compressors is analytically expressed by non-dimensional parameters. An optimal inlet flow angle is derived to achieve the maximum swallowing capacity under a certain inlet relative Mach number, which leads to the minimum energy loss and a more compact geometry for the compressor. Secondly, a new condensation design principle is developed by proposing a novel concept of the two-zone inlet total condition for SCO2\ua0compressors. In this new principle, the acceptable acceleration margin (AAM) is derived as a criterion to limit the impeller inlet condensation. The present inlet design method is validated in the design and simulation of a low-flow-coefficient compressor stage based on the real gas model. The mechanisms of flow accelerations in the impeller inducer, which form low-pressure regions and further produce condensation, are analyzed and clarified under different operating conditions. It is found that the proposed method is efficient to limit the condensation in the impeller inducer, keep the compactness of the compressor, and maintain a high cycle efficiency

Class-Incremental Learning based on Label Generation

Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF). This paper reports our finding that if we formulate CIL as a continual label generation problem, CF is drastically reduced and the generalizable representations of pre-trained models can be better retained. We thus propose a new CIL method (VAG) that also leverages the sparsity of vocabulary to focus the generation and creates pseudo-replay samples by using label semantics. Experimental results show that VAG outperforms baselines by a large margin.Comment: 12 pages, ACL 2023 Main Conferenc

An Improved Phase-Locked-Loop Control with Alternative Damping Factors for VSC Connected to Weak AC System

The gains of phase-locked-loop (PLL) have significant impacts on the power transfer limits for the voltage source converter (VSC) connected to weak AC system. Therefore, in this paper, an improved PLL control, respectively, with alternative damping factors for rectifier and inverter is proposed. First, it is proved that the impedance angle of AC system has a great impact on the small-signal stability of the VSC system. With the same variation tendency of Thévenin equivalent resistance, the limits of power transmission are changing in opposite trends for rectifier and inverter. Second, the improved PLL with alternative damping factors is proposed based on the participation factor analysis. Third, the optimal damping factors of the improved PLL control for rectifier and inverter are calculated. Simulations and calculations validated the following three conclusions: (1) in rectifying operation, the equivalent system resistance has a negative impact on the stability of the system and this is not the case for inverting operation; (2) adding the alternative damping factors to PLL control shows similar results compared with changing the impedance angle of AC system; (3) the proposed optimal damping factors of PLL can effectively extend the power transfer limits under both rectifier and inverter modes

Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.Comment: 7 pages, 11 figures, accepted by the 2019 World Wide Web Conference (WWW '19