Evolution of Classical and Quantum States in the Groupoid Picture of Quantum Mechanics

The evolution of states of the composition of classical and quantum systems in the groupoid formalism for physical theories introduced recently is discussed. It is shown that the notion of a classical system, in the sense of Birkhoff and von Neumann, is equivalent, in the case of systems with a countable number of outputs, to a totally disconnected groupoid with Abelian von Neumann algebra. The impossibility of evolving a separable state of a composite system made up of a classical and a quantum one into an entangled state by means of a unitary evolution is proven in accordance with Raggio’s theorem, which is extended to include a new family of separable states corresponding to the composition of a system with a totally disconnected space of outcomes and a quantum one.     

基于平面度的燃料电池电化学性能模拟

 固体氧化物燃料电池的翘曲会影响电极-盖板界面的接触情况，从而影响电化学性能，对相关制造工艺提出了很大的挑战．为了分析燃料电池平面度对放电过程的影响，揭示其潜在的风险，我们建立了两个基于有限元法的仿真模型，对考虑平面度缺陷的燃料电池封装和放电进行分析．在对固体氧化物燃料电池进行平面度测量的基础上，首先建立了具有真实燃料电池翘曲特性的几何模型，分析封装过程中接触压力的分布情况．然后将接触电阻的仿真结果导入到三维多物理场耦合模型中，模拟具有平面度缺陷的燃料电池电化学性能．计算结果展示了燃料电池两侧封装过程中接触压力的分布情况．通过对比有接触电阻和无接触电阻的燃料电池电流密度，分析了电池与盖板的接触对放电过程的影响．结果表明，燃料电池的凹陷面较难达到满意的接触状态，需要比凸起面更大的封装压力．燃料电池表面接触电阻的变化将引起电流传导路径的变化，产生局部高电流或低电流．这项工作强调了在燃料电池中保持均匀分布的接触电阻的重要性，为今后的优化工作奠定了基础．     

环境规制强度影响能源效率的门槛效应研究——基于PSTR模型

利用非径向-SBM和CCR模型测算了我国30个省市(暂不包括西藏和港澳台地区)2007-2016年间的环境规制强度以及能源效率,构建面板平滑转移回归模型(PSTR),分析了环境规制强度对我国能源效率的连续非线性变化的影响.研究结果显示,全国环境规制强度对我国能源效率存在非线性效应,二者之间呈倒“U”型关系.环境规制强度小于门槛值0.6376时,环境规制对能源效率产生创新补偿效应,当环境规制强度高于门槛值0.6376时,环境规制对能源效率产生遵循成本效应.而我国东西部地区的环境规制强度与地区能源效率间却存在着与之相反的“U”型效应.     

Existence of multiple and sign-changing solutions for a second-order nonlinear functional difference equation with periodic coefficients

In the present paper, we apply the method of invariant sets of descending flow to establish a series of criteria to ensure that a second-order nonlinear functional difference equation with periodic boundary conditions possesses at least one trivial solution and three nontrivial solutions. These nontrivial solutions consist of sign-changing solutions, positive solutions and negative solutions. Moreover, as an application of our theoretical results, an example is elaborated. Our results generalize and improve some existing ones.     

Universality of deep convolutional neural networks

Deep learning has been widely applied and brought breakthroughs in speech recognition, computer vision, and many other domains. Deep neural network architectures and computational issues have been well studied in machine learning. But there lacks a theoretical foundation for understanding the approximation or generalization ability of deep learning methods generated by the network architectures such as deep convolutional neural networks. Here we show that a deep convolutional neural network (CNN) is universal, meaning that it can be used to approximate any continuous function to an arbitrary accuracy when the depth of the neural network is large enough. This answers an open question in learning theory. Our quantitative estimate, given tightly in terms of the number of free parameters to be computed, verifies the efficiency of deep CNNs in dealing with large dimensional data. Our study also demonstrates the role of convolutions in deep CNNs.     

Information percolation and cutoff for the random‐cluster model

We consider the random‐cluster model (RCM) on with parameters p∈(0,1) and q ≥ 1. This is a generalization of the standard bond percolation (with edges open independently with probability p) which is biased by a factor q raised to the number of connected components. We study the well‐known Fortuin‐Kasteleyn (FK)‐dynamics on this model where the update at an edge depends on the global geometry of the system unlike the Glauber heat‐bath dynamics for spin systems, and prove that for all small enough p (depending on the dimension) and any q>1, the FK‐dynamics exhibits the cutoff phenomenon at with a window size , where λ_∞ is the large n limit of the spectral gap of the process. Our proof extends the information percolation framework of Lubetzky and Sly to the RCM and also relies on the arguments of Blanca and Sinclair who proved a sharp mixing time bound for the planar version. A key aspect of our proof is the analysis of the effect of a sequence of dependent (across time) Bernoulli percolations extracted from the graphical construction of the dynamics, on how information propagates.