光的直线传播在地球测量中的应用

以著名的十大物理实验之一埃拉托色尼测量地球周长为例,总结了4种测量地球半径的简单物理方法.利用光的直线传播和太阳光可以看作平行光的物理知识,通过人眼观察,辅以直尺、绳索、计时器、水容器等简单的测量工具,经过几何光学计算,就可以比较准确地得到地球的周长或半径,并分析了测量中误差产生的原因.实验中对物理概念及规律的应用,对...     

On timing Jitter in wavelength-division multiplexed soliton systems

Collision-induced timing shifts in a wavelength-division multiplexed soliton system are computed when damping, amplification, filtering and positive dispersion management following the loss profile are included. A statistical analysis is presented which takes into account the resulting effect of the large number of collisions occurring in the fiber. Analytic expressions are derived for the root mean square timing jitter and the maximum length of error-free transmission with an arbitrary number of channels. An extensive analysis of system performance corresponding to situations with and without filters and/or dispersion management is carried out.     

Stability of fundamental solitons of coupled nonlinear Schrödinger equations

We present analytical stability criteria for the fundamental solitons of two coupled nonlinear Schrödinger equations. The derived stability criteria are applied to the coupled fundamental soliton states in isotropic nonlinear media, in birefringent fibres and in nonlinear couplers. The predictions from the analytical stability criteria are consistent with numerical results.     

浅议物理实验教学环节改革

为进一步提高大学物理实验的教学质量，本文就实验课的预习、操作、实验报告的撰写及考核方法进行了探讨。     

The Sharma-Tasso-Olver-Burgers equation: its conservation laws and kink solitons

The present paper deals with the Sharma–Tasso–Olver–Burgers equation (STOBE) and its conservation laws and kink solitons. More precisely, the formal Lagrangian, Lie symmetries, and adjoint equations of the STOBE are firstly constructed to retrieve its conservation laws. Kink solitons of the STOBE are then extracted through adopting a series of newly well-designed approaches such as Kudryashov and exponential methods. Diverse graphs in 2 and 3D postures are formally portrayed to reveal the dynamical features of kink solitons. According to the authors’ knowledge, the outcomes of the current investigation are new and have been listed for the first time.     

有限温度张量重正化群方法及其在阻挫量子磁性研究中的应用

阻挫量子磁体中的新奇物态与效应是凝聚态物理研究的重要前沿方向,因其与高温超导、拓扑量子计算等的密切联系,近年来吸引了人们浓厚的研究兴趣。实验上,阻挫自旋液体候选材料的 新进展层出不穷,人们系统地研究了若干三角晶格、笼目晶格和六角Kitaev 阻挫磁体等材料,发 现其在一定条件下展现出自旋液体态的特征,但澄清其中的量子物态是充满挑战的量子多体问题。 作者最近的工作指出,可以从有限温度张量重正化群多体计算入手,开展热力学性质的精确计算 与分析,确定阻挫磁体的微观自旋模型,做出进一步理论预言并开展实验验证,从而建立量子磁性 系统的多体计算精确研究方案。有限温度张量重正化群方法是计算大尺寸二维阻挫量子自旋模型 有限温度性质的有力工具,在本文中作者首先介绍新近发展的系列张量重正化群方法,包括线性 和指数张量重正化群等。随后,作者讨论有限温度张量方法在三角晶格量子伊辛磁体TmMgGaO4 和六角晶格Kitaev 磁体α-RuCl3 的微观自旋模型中的具体应用：通过高精度和全面的多体计算, 揭示出其中存在演生U(1) 对称性与拓扑相变,以及高场量子自旋液体态等新颖的结论,这些理 论预言也陆续被实验所证实。通过上述实例,作者展示了有限温度张量重正化群计算方法在自旋 液体候选材料研究中的应用价值,并期待这些方法能在强关联量子物质研究中发挥重要作用。     

Brain-like synaptic memristor based on lithium-doped silicate for neuromorphic computing

Artificial synapse is one of the potential electronics for constructing neural network hardware. In this work, Pt/LiSiO_x/TiN analog artificial synapse memristor is designed and investigated. With the increase of compliance current (C. C.) under 0.6 mA, 1 mA, and 3 mA, the current in the high resistance state (HRS) presents an increasing variation, which indicates lithium ions participates in the operation process for Pt/LiSiO_x/TiN memristor. Moreover, depending on the movement of lithium ions in the functional layer, the memristor illustrates excellent conduction modulation property, so the long-term potentiation (LTP) or depression (LTD) and paired-pulse facilitation (PPF) synaptic functions are successfully achieved. The neural network simulation for pattern recognition is proposed with the recognition accuracy of 91.4%. These findings suggest the potential application of the LiSiO_x memristor in the neuromorphic computing.     

An Edge Server Placement Method Based on Reinforcement Learning

In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. To overcome these defects, we propose a novel edge server placement algorithm based on deep q-network and reinforcement learning, dubbed DQN-ESPA, which can achieve optimal placements without relying on previous placement experience. In DQN-ESPA, the edge server placement problem is modeled as a Markov decision process, which is formalized with the state space, action space and reward function, and it is subsequently solved using a reinforcement learning algorithm. Experimental results using real datasets from Shanghai Telecom show that DQN-ESPA outperforms state-of-the-art algorithms such as simulated annealing placement algorithm (SAPA), Top-K placement algorithm (TKPA), K-Means placement algorithm (KMPA), and random placement algorithm (RPA). In particular, with a comprehensive consideration of access delay and workload balance, DQN-ESPA achieves up to 13.40% and 15.54% better placement performance for 100 and 300 edge servers respectively.     

Latency minimization for IRS-assisted mobile edge computing networks

In this paper, we consider the latency minimization problem via designing intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) networks. For the scene when local users cannot complete all computing tasks independently, a common solution is transferring tasks to cloud servers. We consider that the MEC system contains multiple independent users, and each user sends task data to the base station in a partially offloaded manner. Our goal is to minimize the maximum latency for all users. The original problem is strongly non-convex, which caused difficulty to solve. We first introduce a new variable to transform the max–min problem into an alternative minimization problem, and then solve each optimization variable separately by the block coordinate descent method. Finally, our simulation experiments demonstrate that our proposed scheme obtain better performance with respect to other existing schemes.     

Contract-based scheme for computational resource allocation in cloud-assisted parked vehicular edge computing

Having shown promising performance with high flexibility and efficiency in vehicular edge computing (VEC) network, the parked vehicles (PVs) received an increasing number of attentions in recent years. However, PVs’ residual battery power restricts their running time. In addition, there is still no alternate resource pool for the PVs to cope with the emergencies in the previous VEC framework. To alleviate these problems, we model a cloud-assisted parked vehicular edge computing (PVEC) framework, in which the PVs are classified based on their residual battery power. PVs corporate with the cloud servers (CSs) for the computational resources provision. In addition, we formulate the utilities of the service provider (SP) and PVs and design a contract-based resource allocation problem for the maximization of the SP’s utility. Considering that it is intractable to solve the optimization problem directly, the primal problem is simplified and decoupled into two sub-problems. To design the optimal contracts, we solve the sub-problems by Lagrangian multiplier method and dual function. Simulation results prove that the utilities of PVs can reach to the maximum when they choose the contract corresponding to their types. In addition, the simulation results illustrate the superiority of proposed scheme over previous schemes in improving the utilities of the SP and social welfare.