Simultaneous two-way classical communication and measurement-device-independent quantum key distribution on oceanic quantum channels

Simultaneous two-way classical and quantum (STCQ) communication combines both continuous classical coherent optical communication and continuous-variable quantum key distribution (CV-QKD), which eliminates all detection-related imperfections by being measurement-device-independent (MDI). In this paper, we propose a protocol relying on STCQ communication on the oceanic quantum channel, in which the superposition-modulation-based coherent states depend on the information bits of both the secret key and the classical communication ciphertext. We analyse the encoding combination in classical communication and consider the probability distribution transmittance under seawater turbulence with various interference factors. Our numerical simulations of various practical scenarios demonstrate that the proposed protocol can simultaneously enable two-way classical communication and CV-MDI QKD with just a slight performance degradation transmission distance compared to the original CV-MDI QKD scheme. Moreover, the asymmetric situation outperforms the symmetric case in terms of transmission distance and optical modulation variance. We further take into consideration the impact of finite-size effects to illustrate the applicability of the proposed scheme in practical scenarios. The results show the feasibility of the underwater STCQ scheme, which contributes toward developing a global quantum communication network in free space.     

1-3型二维柱形凹面线聚焦换能器声场特性

设计并研制了柱面结构的1-3型复合材料凹面线聚焦换能器,在提高换能器带宽的同时,可以实现声场的线聚焦。将换能器内部PZT柱作为独立声源,应用瑞利积分和叠加原理,推导出了柱形凹面换能器总声场的理论表达式。通过仿真计算分析了换能器在聚焦线上的声场特点以及相关参数对聚焦性能的影响。对换能器参数做出合理设计,使换能器在实现线聚焦的同时,声场在聚焦线上的起伏较小,从而设计并制作出聚焦性能良好的线聚焦凹面换能器探头。实验测试结果表明采用本文方法计算得到的换能器声场与实测的声场分布基本符合,柱形凹面换能器在其几何焦点附近范围内均可实现聚焦,并在侧向上形成清晰的聚焦线,其聚焦线长度为换能器的侧向结构长度,在聚焦线上声场分布起伏较小。     

Improved charge transport and trap-state distribution in donor–acceptor-type semiconducting copolymer with a fluoropolymer dielectric film

This study presents a p-type doping method for donor–acceptor-type conjugated semiconducting copolymer-based field-effect transistors (FETs) with a fluoropolymer dielectric film. The polymeric FET, which initially comprises a non-polar polymer dielectric layer (poly (methyl methacrylate), PMMA), shows ambipolar behavior owing to the well-balanced electron-accepting and -donating properties of the cyclopentadithiophene (CDT) and pyridyl-2,1,3-thiadiazole (PTz)-based conjugated polymer backbone system. However, when combined with an amorphous fluoropolymer (CYTOP) dielectric layer, the FET device exhibits that their ambipolar behavior remarkably changes to a high-performance p-type FET; the hole mobility enhanced by a factor of ~3 and the threshold voltage significantly shifted from −29 V to −12 V. The density of trap states in the CDT-PTz-based polymeric FETs with a CYTOP dielectric layer, which was estimated from the temperature-dependent transfer characteristics, was narrower and shallower than that of polymeric FETs with a PMMA dielectric layer. As such, it can be inferred that the deep-trap states are filled with additional doped charges from the surface polarization induced by the fluorinated dielectrics at the semiconductor-dielectric interface.     

Inverse Bremsstrahlung absorption coefficient in inhomogeneous plasma with nonextensive electron velocity distribution and Tsallis exponential electron density profile

Inverse bremsstrahlung (collisional) absorption of the laser beam is studied in plasma with a generalized (q-nonextensive) electron velocity distribution and some kind of generalized electron density profile. It is shown that for some values of parameters designating the q-nonextensive electron velocity distribution function and its generalized density profile, the calculated absorption coefficient reduces to the already known cases with Maxwellian velocity distribution with linear and exponential density profiles.     

Optimal Randomness for Stochastic Configuration Network (SCN) with Heavy-Tailed Distributions

Stochastic Configuration Network (SCN) has a powerful capability for regression and classification analysis. Traditionally, it is quite challenging to correctly determine an appropriate architecture for a neural network so that the trained model can achieve excellent performance for both learning and generalization. Compared with the known randomized learning algorithms for single hidden layer feed-forward neural networks, such as Randomized Radial Basis Function (RBF) Networks and Random Vector Functional-link (RVFL), the SCN randomly assigns the input weights and biases of the hidden nodes in a supervisory mechanism. Since the parameters in the hidden layers are randomly generated in uniform distribution, hypothetically, there is optimal randomness. Heavy-tailed distribution has shown optimal randomness in an unknown environment for finding some targets. Therefore, in this research, the authors used heavy-tailed distributions to randomly initialize weights and biases to see if the new SCN models can achieve better performance than the original SCN. Heavy-tailed distributions, such as Lévy distribution, Cauchy distribution, and Weibull distribution, have been used. Since some mixed distributions show heavy-tailed properties, the mixed Gaussian and Laplace distributions were also studied in this research work. Experimental results showed improved performance for SCN with heavy-tailed distributions. For the regression model, SCN-Lévy, SCN-Mixture, SCN-Cauchy, and SCN-Weibull used less hidden nodes to achieve similar performance with SCN. For the classification model, SCN-Mixture, SCN-Lévy, and SCN-Cauchy have higher test accuracy of 91.5%, 91.7% and 92.4%, respectively. Both are higher than the test accuracy of the original SCN.     

Calibration Invariance of the MaxEnt Distribution in the Maximum Entropy Principle

The maximum entropy principle consists of two steps: The first step is to find the distribution which maximizes entropy under given constraints. The second step is to calculate the corresponding thermodynamic quantities. The second part is determined by Lagrange multipliers’ relation to the measurable physical quantities as temperature or Helmholtz free energy/free entropy. We show that for a given MaxEnt distribution, the whole class of entropies and constraints leads to the same distribution but generally different thermodynamics. Two simple classes of transformations that preserve the MaxEnt distributions are studied: The first case is a transform of the entropy to an arbitrary increasing function of that entropy. The second case is the transform of the energetic constraint to a combination of the normalization and energetic constraints. We derive group transformations of the Lagrange multipliers corresponding to these transformations and determine their connections to thermodynamic quantities. For each case, we provide a simple example of this transformation.     

Transient Dynamics in the Random Growth and Reset Model

A mean-field type model with random growth and reset terms is considered. The stationary distributions resulting from the corresponding master equation are relatively easy to obtain; however, for practical applications one also needs to know the convergence to stationarity. The present work contributes to this direction, studying the transient dynamics in the discrete version of the model by two different approaches. The first method is based on mathematical induction by the recursive integration of the coupled differential equations for the discrete states. The second method transforms the coupled ordinary differential equation system into a partial differential equation for the generating function. We derive analytical results for some important, practically interesting cases and discuss the obtained results for the transient dynamics.     

抵消Wigner-Ville分布交叉项的窄带运动声源无源测速

利用声波的多普勒频移可以对窄带运动声源进行单传感器无源测速,其性能很大程度上取决于能否精确地估计出声波的瞬时频率.Wigner-Ville分布虽然时频分辨率高,但存在交叉项干扰,很少被直接用于瞬时频率估计。对此,提出了抵消Wigner-Ville分布交叉项的单传感器窄带声源无源测速方法。利用交叉项与声源速度的关系构造一个抵消项,引入到Wigner-Ville分布中,通过对声源速度估计值进行迭代更新,使抵消项与交叉项相位相反,从而约掉交叉项。经实测噪声数据验证,对一辆以6.07 m/s匀速运动的卡车(信噪比约为29 dB)测速误差为0.1 m/s,运行时间为4.6 s,对一架以28.90 m/s匀速运动的直升机(信噪比约为16 dB)测速误差为0.46 m/s,运行时间为1.2 s,均优于匹配Wigner变换和多普勒线性调频小波变换测速方法.     

A Mixture Model of Truncated Zeta Distributions with Applications to Scientific Collaboration Networks

The degree distribution has attracted considerable attention from network scientists in the last few decades to have knowledge of the topological structure of networks. It is widely acknowledged that many real networks have power-law degree distributions. However, the deviation from such a behavior often appears when the range of degrees is small. Even worse, the conventional employment of the continuous power-law distribution usually causes an inaccurate inference as the degree should be discrete-valued. To remedy these obstacles, we propose a finite mixture model of truncated zeta distributions for a broad range of degrees that disobeys a power-law behavior in the range of small degrees while maintaining the scale-free behavior. The maximum likelihood algorithm alongside the model selection method is presented to estimate model parameters and the number of mixture components. The validity of the suggested algorithm is evidenced by Monte Carlo simulations. We apply our method to five disciplines of scientific collaboration networks with remarkable interpretations. The proposed model outperforms the other alternatives in terms of the goodness-of-fit.