Nonlinear Ramsey Interferometry of Fermi Superfluid Gases in a Double-Well Potential

The nonlinear Ramsey interferometry of Fermi superfluid gases in a double-well potential is investigated in this paper. We found that the frequency of the Ramsey fringes exactly reflects the strength of nonlinearity, or the scattering length of the Fermi superfluid gases. The cases of sudden limit, the adiabatic limit and the general case are studied. The analytical result is in good agreement with the numerical ones. The adiabatic condition is proposed. In general situation, the zero-frequency point emerge. Finally the possible applications of the theory are discussed.     

Modeling of polycrystalline ZnO thin-film transistors with a consideration of the deep and tail states

We report a model of the carrier transport and the subgap density of states in a polycrystalline ZnO film for simulating a polycrystalline ZnO thin film transistor. This simple model considering the deep and the band tail states reproduces well the characteristics of polycrystalline ZnO thin film transistors. Furthermore, using the developed model, we study the effects of defect parameters on the electrical performances of the polycrystalline ZnO thin film transistors.     

Electronic properties and deep level transient spectroscopy of CdS/CdTe thin film solar cells

It is well known that preparing temperatures and defects are highly related to deep-level impurities. In our studies, the CdTe polycrystalline films have been prepared at various temperatures by close spaced sublimation (CSS). The different preparing temperature effects on CdS/CdTe solar cells and deep-level impurities have been investigated by I--V and C--V measurements and deep level transient spectroscopy (DLTS). By comparison, less dark saturated current density, higher carrier concentration, and better photovoltaic performance are demonstrated in a 580oC sample. Also there is less deep-level impurity recombination, because the lower hole trap concentration is present in this sample. In addition, three deep levels, E_v+0.341 eV(H4), E_v+0.226 eV(H5) and E_C-0.147 eV(E3), are found in the 580oC sample, and the possible source of deep levels is analysed and discussed.     

CsLiB6O10晶体光学参变振荡器的光学特性

根据三波耦合过程中的能量和动量守恒、晶体的塞耳迈耶尔色散方程,通过数值模拟计算了213nm作CLBO光学参变振荡抽运源时,分别得到了在Ⅰ类和Ⅱ类相位匹配时参量光调谐范围为237～289nm、807～2793nm和404～2800nm。获得了在Ⅰ类匹配时的单谐振或双谐振腔的CLBO光学参变振荡转换效率都大于同等条件下的BBO光学参变振荡,Ⅱ类匹配时,CLBO晶体的转换效率略小于BBO晶体,但是CLBO光学参变振荡转换效率的最大值较BBO光学参变振荡出现于更短的紫外波段。从理论上证明了CLBO晶体是优质的深紫外透光波段非线性光学晶体。     

聚氧化乙烯表面修饰对ZnO光学性质的影响

利用聚氧化乙烯(PEO)对ZnO表面进行修饰,研究有机包覆对ZnO光学性质的影响。用吸收光谱和光致发光光谱来表征和研究PEO包覆对ZnO纳米粒子光学性质的影响。吸收光谱结果表明随着ZnO薄膜中PEO含量的增加,激子吸收峰逐渐向低能侧方向移动。光致发光光谱是由紫外发射和与氧空位有关的深能级缺陷发光组成,且随着ZnO薄膜中PEO含量的增加,紫外发射与深能级发光强度之比逐渐增大,当ZnO薄膜中的PEO达到最大值时,其比值为31．5,远远大于纯ZnO薄膜的紫外发射与深能级发光强度之比1．04。由此可见,ZnO被PEO包覆后,提高了紫外发光效率,改善了ZnO薄膜的质量。     

A Comparison Study of Deep Galerkin Method and Deep Ritz Method for Elliptic Problems with Different Boundary Conditions

Recent years have witnessed growing interests in solving partial differential equations by deep neural networks, especially in the high-dimensional case. Unlike classical numerical methods, such as finite difference method and finite element method, the enforcement of boundary conditions in deep neural networks is highly nontrivial. One general strategy is to use the penalty method. In the work, we conduct a comparison study for elliptic problems with four different boundary conditions, i.e., Dirichlet, Neumann, Robin, and periodic boundary conditions, using two representative methods: deep Galerkin method and deep Ritz method. In the former, the PDE residual is minimized in the least-squares sense while the corresponding variational problem is minimized in the latter. Therefore, it is reasonably expected that deep Galerkin method works better for smooth solutions while deep Ritz method works better for low-regularity solutions. However, by a number of examples, we observe that deep Ritz method can outperform deep Galerkin method with a clear dependence of dimensionality even for smooth solutions and deep Galerkin method can also outperform deep Ritz method for low-regularity solutions.Besides, in some cases, when the boundary condition can be implemented in an exact manner, we find that such a strategy not only provides a better approximate solution but also facilitates the training process.     

Community detection-based deep neural network architectures: A fully automated framework based on Likert-scale data

Deep neural networks (DNNs) have emerged as a state-of-the-art tool in very different research fields due to its adaptive power to the decision space since they do not presuppose any linear relationship between data. Some of the main disadvantages of these trending models are that the choice of the network underlying architecture profoundly influences the performance of the model and that the architecture design requires prior knowledge of the field of study. The use of questionnaires is hugely extended in social/behavioral sciences. The main contribution of this work is to automate the process of a DNN architecture design by using an agglomerative hierarchical algorithm that mimics the conceptual structure of such surveys. Although the train had regression purposes, it is easily convertible to deal with classification tasks. Our proposed methodology will be tested with a database containing socio-demographic data and the responses to five psychometric Likert scales related to the prediction of happiness. These scales have been already used to design a DNN architecture based on the subdimension of the scales. We show that our new network configurations outperform the previous existing DNN architectures.     

强化学习在运筹学的应用:研究进展与展望

强化学习已经成为人工智能领域一个新的研究热点,并已成功应用于各领域,强化学习将运筹优化领域的很多问题视为序贯决策问题,建模为马尔可夫决策过程并进行求解,在求解复杂、动态、随机运筹优化问题具有较大的优势。本文主要对强化学习在运筹优化领域的应用进行综述,首先介绍了强化学习的基本原理及其应用于运筹优化领域的研究框架,然后回顾并总结了强化学习在库存控制、路径优化、装箱配载和车间作业调度等方面的研究成果,并将最新的深度强化学习以及传统方法在运筹学领域的应用研究进行了对比分析,以突出深度强化学习的优越性。最后提出几个值得进一步探讨的研究方向,期望能为强化学习在运筹优化领域的研究提供参考。     

Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset

Transient noise appearing in the data from gravitational-wave detectors frequently causes problems, such as instability of the detectors and overlapping or mimicking gravitational-wave signals. Because transient noise is considered to be associated with the environment and instrument, its classification would help to understand its origin and improve the detector's performance. In a previous study, an architecture for classifying transient noise using a time–frequency 2D image (spectrogram) is proposed, which uses unsupervised deep learning combined with variational autoencoder and invariant information clustering. The proposed unsupervised-learning architecture is applied to the Gravity Spy dataset, which consists of Advanced Laser Interferometer Gravitational-Wave Observatory (Advanced LIGO) transient noises with their associated metadata to discuss the potential for online or offline data analysis. In this study, focused on the Gravity Spy dataset, the training process of unsupervised-learning architecture of the previous study is examined and reported.