3-[1-(4-Chloro­phen­yl)eth­yl]-1,3-thia­zinane-2-thione

In the title compound, C12H14ClNS2, the thia­zole ring adopts an envelope conformation; the basal plane is nearly perpendicular to the benzene ring at a dihedral angle of 85.72 (5)°. Weak inter­molecular C—H⋯S hydrogen bonding is present in the crystal structure

Ghost imaging lidar via sparsity constraints

For remote sensing, high-resolution imaging techniques are helpful to catch more characteristic information of the target. We extend pseudo-thermal light ghost imaging to the area of remote imaging and propose a ghost imaging lidar system. For the first time, we demonstrate experimentally that the real-space image of a target at about 1.0 km range with 20 mm resolution is achieved by ghost imaging via sparsity constraints (GISC) technique. The characters of GISC technique compared to the existing lidar systems are also discussed.Comment: 4pages, 3figure

Ghost imaging without beam splitter

Many significant results have been achieved in the fields of ghost imaging, in which the beam splitter is an indispensable optical component. This paper introduces a method to realize ghost imaging without beam splitter. And we study this method experimentally and theoretically. Finally, we suggest that our device can be applied to implement the ghost imaging when we use the Sun light as the light source

Zc(3900)Z_c(3900) as a DDˉ∗D\bar{D}^* molecule from the pole counting rule

A comprehensive study on the nature of the $Z_c(3900)$ resonant structure is carried out in this work. By constructing the pertinent effective Lagrangians and considering the important final-state-interaction effects, we first give a unified description to all the relevant experimental data available, including the $J/\psi\pi$ and $\pi\pi$ invariant mass distributions from the $e^+e^-\to J/\psi\pi\pi$ process, the $h_c\pi$ distribution from $e^+e^-\to h_c\pi\pi$ and also the $D\bar D^{*}$ spectrum in the $e^+e^-\to D\bar D^{*}\pi$ process. After fitting the unknown parameters to the previous data, we search the pole in the complex energy plane and find only one pole in the nearby energy region in different Riemann sheets. Therefore we conclude that $Z_c(3900)$ is of $D\bar D^*$ molecular nature, according to the pole counting rule method~[Nucl.~Phys.~A543, 632 (1992); Phys.~Rev.~D 35,~1633 (1987)]. We emphasize that the conclusion based upon the pole counting method is not trivial, since both the $D\bar D^{*}$ contact interactions and the explicit $Z_c$ exchanges are introduced in our analyses and they lead to the same conclusion.Comment: 21 pages, 9 figures. To match the published version in PRD. Additional discussion on the spectral density function is include

Operator based Robust Nonlinear Control Design to an Ionic Polymer Metal Composite with Uncertainties and Input Constraints

In this paper, robust nonlinear control design to an ionic polymer metal composite (IPMC) with uncertainties and input constraints is studied. The IPMC is a novel smart polymer material, and many potential applications for low mass high displacement actuators in biomedical and robotic systems have been shown. In general, the IPMC has highly nonlinear property, and the control input is subject to some constraints to ensure safety and longer service life of IPMC. Moreover, there exist uncertainties caused by identifying some physical parameters and approximate calculation in dynamic model. As a result, considering measurement error of parameters and model error, a practical nonlinear model is obtained, and a nonlinear robust control design with uncertainties and input constraints using operator-based robust right coprime factorization is proposed. The effectiveness of the proposed control method based on obtained nonlinear model is confirmed by simulation and experimental results

An Improved Electrical Switching and Phase-Transition Model for Scanning Probe Phase-Change Memory

Scanning probe phase-change memory (SPPCM) has been widely considered as one of the most promising candidates for next-generation data storage devices due to its fast switching time, low power consumption, and potential for ultra-high density. Development of a comprehensive model able to accurately describe all the physical processes involved in SPPCM operations is therefore vital to provide researchers with an effective route for device optimization. In this paper, we introduce a pseudo-three-dimensional model to simulate the electrothermal and phase-transition phenomena observed during the SPPCM writing process by simultaneously solving Laplace’s equation to model the electrical process, the classical heat transfer equation, and a rate equation to model phase transitions. The crystalline bit region of a typical probe system and the resulting current-voltage curve obtained from simulations of the writing process showed good agreement with experimental results obtained under an equivalent configuration, demonstrating the validity of the proposed model

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification

The dynamic ensemble selection of classifiers is an effective approach for processing label-imbalanced data classifications. However, such a technique is prone to overfitting, owing to the lack of regularization methods and the dependence of the aforementioned technique on local geometry. In this study, focusing on binary imbalanced data classification, a novel dynamic ensemble method, namely adaptive ensemble of classifiers with regularization (AER), is proposed, to overcome the stated limitations. The method solves the overfitting problem through implicit regularization. Specifically, it leverages the properties of stochastic gradient descent to obtain the solution with the minimum norm, thereby achieving regularization; furthermore, it interpolates the ensemble weights by exploiting the global geometry of data to further prevent overfitting. According to our theoretical proofs, the seemingly complicated AER paradigm, in addition to its regularization capabilities, can actually reduce the asymptotic time and memory complexities of several other algorithms. We evaluate the proposed AER method on seven benchmark imbalanced datasets from the UCI machine learning repository and one artificially generated GMM-based dataset with five variations. The results show that the proposed algorithm outperforms the major existing algorithms based on multiple metrics in most cases, and two hypothesis tests (McNemar's and Wilcoxon tests) verify the statistical significance further. In addition, the proposed method has other preferred properties such as special advantages in dealing with highly imbalanced data, and it pioneers the research on the regularization for dynamic ensemble methods.Comment: Major revision; Change of authors due to contribution