探究式教学下的物理演示实验

通过实验进行科学探究式的学习是本次课程改革方式最明显的变革．探究式教学也要求演示实验如何能更好的从教师走向学生，让学生更贴近真实的物理概念和定律，更自然的学好物理．     

探究式教学在物理教学中的实施研究

新一轮国家基础教育课程改革的一个重要而具体的目标,就是要改变至今仍普遍存在的学生被动接受、大运动量反复操练的学习方式,倡导学生主动参与的探究学习。1探究式教学的本质所谓探究式课堂教学,就是以探究为主的教学。具体说它是指教学过程是在教师的启发诱导下,以学生独立自主学习和合作讨论为前提,以现行教材为基本     

从自组织理论的视角看探究式学习与接受式学习

目前人们对探究式学习与接受式学习在教学中的地位认识仍不清晰，在具体的实践教学中存在不少误区。本文试图运用白组织理论对探究式学习和接受式学习进行辨析，并得出这两种学习方式都是使学生学习知识的有效手段，而它们的区别在于接受式学习强调依靠外力来推动学生的学习，探究式学习则强调依靠学生系统内部的自组织演化机制来促进学生自主的学习。     

谈探究式《科学》课堂的合理引导

培养学生的探究能力是初中<科学>课程教学的教学目标之一,故探究式课堂教学必不可少.探究式课堂教学就是把科学探究引进课堂,其目的是让学生能像科学家那样独立地通过探究活动来获得知识,而不是教师安排好一切,让学生顺着预定的途径学习或把学生径直引向答案.     

WebQuest——一种探究式的学习

本介绍了WebQuest的的教学形式，介绍了WebQuest的基本组成，通过几个有特色的例子，说明WebQuestt是一种新的探究式的学习，在新的课程标准下，对课程的改革具有一定的参考价值。     

物理教学与研究性学习整合的实践研究

所谓研究性学习是指在教师的指导下，从自然、社会和生活中选择和确定专题进行研究，并在研究过程中主动地获取知识、应用知识、解决问题的学习活动．这种课程形态的核心是要改变学生的学习方式，强调一种主动探究式的学习，培养学生的创新精神和实践能力．     

艺术性的语言是缔造高效物理课堂的基石

在一些大学里，物理教学的方式还是传统的填鸭式，这不利于对学生创新能力和自主性学习的培养。本文首先介绍一下探究式教学以及探究式教学的意义，再从其他的几个方面详细的阐述探究式教学的具体实施措施。     

大学物理探究式教学

在一些大学里,物理教学的方式还是传统的填鸭式,这不利于对学生创新能力和自主性学习的培养。本文首先介绍一下探究式教学以及探究式教学的意义,再从其他的几个方面详细的阐述探究式教学的具体实施措施。     

高中物理探究式教学三个问题探析

随着基础教育课程改革的深入发展，全国大部分省市普通高中已进入新课改．新课改的一项重要举措是设计模块化课程供学生选择学习，其目的在于引发学习方式和教学方式的变革．然而，课堂教学是推进素质教育的主渠道，要真正落实新课改理念，需要深入开展课堂教学改革．从国内外教育改革的发展趋势和研究成果来看，以科学探究的思想指导课堂教学改革，会扩大学生的参与程度，增强学生学习的主动性，有利于开发学生的创造潜能和创新精神．从上世纪60年代开始，国内外便悄然兴起了探究式教学的热潮．由于探究式教学思想与目前正如火如荼地开展的新课改的理念相吻合，于是探究式教学再次得到普遍的认同，理所当然地成为撬动新课改的最佳支点；同时新课改也赋予它新的内涵．目前，探究式教学已成了贯彻新课改理念的主旋律，它使课堂教学焕发出新的活力．笔者根据多年的教学实践，结合自己学习《普通高中物理课程标准（实验）》的体会，对新课标下高中物理探究式教学的三个问题进行了探析，作为抛砖引玉，现谈—孔之见，以期达到对探究式教学进行深入研究与实践，进一步提高探究教学质量．     

高职物理课“探究式学习”的实践与思考

在新课改的背景下,高职教育呼唤自主学习,物理课堂采用探究式学习法,既能增强高职生的自学能力,又培养学生的创新意识,更有助于学生形成良好的学习态度.探究式学习由提出问题——鼓励学生提出猜想——收集信息——整合信息——得出结论几部分组成.这种学习方式强调学生独立思考,在合作中发现解决问题的办法,是一种积极的学习过程.     

Mode Suppression Phenomenon in a Mode Splitting He-Ne Laser

The mode competition is significant in laser physics and applications. A priority frequency will suppress, in competition, a potential frequency within gain profile, and this becomes more obvious with greatly decreasing the spacing of the two frequencies. The small spacing is produced by splitting one mode into two orthogonally polarized frequencies in a 0. 6328 μm He-Ne laser with a short cavity length (150 mm long). By tuning the cavity length, the intensities of the two frequencies (e-mode and o-mode) under a ariety of frequency spacings is measured. The suppression bandwidth for the potential mode is up to several hundreds of MHz and changes with the frequency spacing of the pair of frequencies. The intensities for the two frequencies are vary oppositely.     

A Manifold Learning Perspective on Representation Learning: Learning Decoder and Representations without an Encoder

Autoencoders are commonly used in representation learning. They consist of an encoder and a decoder, which provide a straightforward method to map n-dimensional data in input space to a lower m-dimensional representation space and back. The decoder itself defines an m-dimensional manifold in input space. Inspired by manifold learning, we showed that the decoder can be trained on its own by learning the representations of the training samples along with the decoder weights using gradient descent. A sum-of-squares loss then corresponds to optimizing the manifold to have the smallest Euclidean distance to the training samples, and similarly for other loss functions. We derived expressions for the number of samples needed to specify the encoder and decoder and showed that the decoder generally requires much fewer training samples to be well-specified compared to the encoder. We discuss the training of autoencoders in this perspective and relate it to previous work in the field that uses noisy training examples and other types of regularization. On the natural image data sets MNIST and CIFAR10, we demonstrated that the decoder is much better suited to learn a low-dimensional representation, especially when trained on small data sets. Using simulated gene regulatory data, we further showed that the decoder alone leads to better generalization and meaningful representations. Our approach of training the decoder alone facilitates representation learning even on small data sets and can lead to improved training of autoencoders. We hope that the simple analyses presented will also contribute to an improved conceptual understanding of representation learning.     

Mode Suppression Phenomenon in a Mode Splitting He-Ne Laser

The mode competition is significant in laser physics and applications. A priority frequency will suppress, in competition, a potential frequency within gain profile, and this becomes more obvious with greatly decreasing the spacing of the two frequencies. The small spacing is produced by splitting one mode into two orthogonally polarized frequencies in a 0. 6328 μm He-Ne laser with a short cavity length (150 mm long). By tuning the cavity length, the intensities of the two frequencies (e-mode and o-mode) under a ariety of frequency spacings is measured. The suppression bandwidth for the potential mode is up to several hundreds of MHz and changes with the frequency spacing of the pair of frequencies. The intensities for the two frequencies are vary oppositely.     

Polarization Mode Dispersion Probability Distribution for Arbitrary Mode Coupling

The probability distribution of the differential group delay for arbitrary mode coupling is simulated with Monte-Carlo method. Fitting the simulation results, we obtain probability distribution function for arbitrary mode coupling.     

Mode order converter

We report experimental results with a simple mode order converter for dielectric slab waveguides. In particular we have shown conversion of higher order modes to the only mode of a monomode waveguide.     

Chiral-Quantum-Optics-Based Supervised Learning

Based on the excellent control of single photons realized by atom-photon-chiral couplings,a novel quantum-optics scheme for supervised learning is proposed. The single-photon rotating and phase-shift operations, which can be controlled by another single photon, are realized by proper atom-photon-chiral couplings. Then, an algorithm to perform the supervised learning tasks, composed by integrating the realized gates, and implemented by the tunable gate parameters, is realized.     

不同模式耦合下偏振模色散几率分布的研究

研究了偏振模色散的统计特性,在不同模式耦合条件下,应用蒙特-卡罗方法模拟偏振模色散矢量的几率分布,并对模拟结果进行了函数拟合。发现随着耦合次数增加差分群时延的几率分布从一个类似δ函数分布逐渐过渡到麦克斯韦分布;在一定的耦合下,几率分布可以呈现高斯分布。对偏振模矢量的两个方向余弦进行统计分析,发现随着耦合次数的增加,偏振模矢量两个方向余弦函数的分布从高斯分布和δ函数分布都变为均匀分布。     

平顶高斯光束的模相关系数和模分解理论

利用部分相干光理论,推导出柱坐标系下平顶高斯光束模相关系数的解析表达式,基于推导出的Ｍ＾２因子的推广公式,建立了平顶高斯光束的模分解理论。     

单模光纤中偏振模色散的仿真模型

利用琼斯矩阵法研究了长单模光纤中偏振模色散的仿真模型.考虑到偏振模色散的随机性,该模型中单模光纤被看作是一系列短双折射光纤段的级联,相邻两段之间耦合角是随机的.研究结果表明,当短双折射光纤段等长时,偏振模色散呈现随波长周期性变化的特点;不符合实际情况.当短双折射光纤段不等长且服从高斯分布时,周期性逐渐消失;当其长度均方差为均值的20%,周期性完全消失.最后比较了偏振模色散的时域统计特性.取短双折射光纤段的长度服从高斯分布且均方差为均值的20%,偏振模色散的统计特性接近于实际分布.因此得出结论:为了正确估计偏振模色散的影响,在单模光纤的级联模型中,短双折射光纤段的长度应服从高斯分布,均方差为其均值的20%.     

单模光纤中的二阶偏振模色散及其统计特性

用琼斯矩阵本征分析法和邦加球法,分别测量了22 km和75 km G.652单模光纤中的二阶偏振模色散,并对其平行分量、垂直分量、PCD（Polarization dependent chromatic dispersion）、消偏振项、各个分量的统计特性及其影响进行了详细的分析,得到了二阶偏振模色散随波长的分布情况及统计特性. 从统计结果可以得到,与PCD项相比,消偏振项在二阶偏振模色散中起主要作用. 该研究对二阶偏振模色散的补偿有一定的指导意义.