Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions

A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote.     

Power-law decay of the view times of scientific courses on YouTube

The temporal power-law decay is one class of interesting decay processes, usually indicating a long-time correlation and benefiting for a system to perform functions in various time-scales. In this work, I collect the data of the view times versus lectures of some scientific courses on YouTube, according to some special principles. These data can reflect the dynamical property of the spontaneous learning behavior, influenced by the decay of learning interest. The view times versus lectures show an obviously power-law decay process. The power approximates to 1, a universal constant. This finding brings the learning process into the interesting power-law family. It will be of interest in the fields of the human dynamics, psychology and education.     

复变函数教学法探讨

复变函数是高等院校工科专业一门重要的基础理论课,它是高等数学的扩展.本文主要探讨在复变函数的教学过程中,教师可以利用"类比"、"转化"和"反例"等教学方法,使学生达到巩固旧知识、探索新课程的目的.     

概率统计课程中研究性学习方法探讨

研究性学习的关键问题之一是充分调动学生主动学习的兴趣与热情,提高学生学习效率,变被动学习为主动学习.结合实例给出了四种激发学生学习兴趣的研究性学习方法,即逆向思维、发散思维、基于Matlab的验证学习以及基于实际问题的教学研究方法.这四种学习方式的有机结合能在理论和实践上有效激发学生主动学习热情及学习兴趣,从而大大提高学生学习效率.     

Level of Inquiry as Motivator in an Inquiry Methods Course for Preservice Elementary Teachers

Of great importance for achieving science education reform may be teachers' interest in science and enjoyment of science. This study explores the motivational qualities (rated for interest, fun, and learning value) of different levels of inquiry of hands‐on class activities. The participants, 53 preservice teachers in two sections of a science methods course, rated the activities at the end of each class. At the end of the course, these activities were categorized by level of inquiry (levels 0–3), with 30% rated as level 0 (no inquiry), 40% as level 1, 22% as level 2, and 8% as level 3, according to how much choice was given for posing questions and designing investigations. Ratings of each hands‐on activity indicated that participants perceived activities of higher levels of inquiry to be more fun and more interesting. They also perceived that they had learned more. These findings suggest that course instructors should determine level of inquiry when planning course activities, and degree of participant input into course activities may be important in the development of interest in science. A focus on hands‐on learning especially at higher levels of inquiry may serve both to capture the interest of teachers and to model how they can make science more authentic and engaging for children.     

基于流形学习的非线性维数约简方法

流形学习是一种新的非线性维数约简方法,近年来正引起可视化等领域研究者的高度重视.为加深对流形学习的理解,介绍了流形学习的基本原理,总结了其研究进展和分类方法,最后阐述了几种常用的流形学习方法的基本思想、算法步骤和各自的优缺点.通过在人工数据集Swiss-Roll上进行实验,将各类方法在近邻值选取和噪声影响等方面进行了对比分析,结果表明:与传统的线性维数约简方法相比,流形学习方法能够有效地发现观测样本的低维结构.最后对流形学习未来的研究方向作出展望,以期在这一领域取得更大进展.     

基于Isomap算法的恒星光谱离群点挖掘

如何从已分类的海量光谱中发现被错分的光谱一直是天文数据处理专家重点研究的问题,探讨的Isomap算法在该问题方面有很好的表现。通过Isomap算法与主成分分析方法(PCA)算法的实验结果对比发现：(1)PCA将具有不同特征的光谱投影到邻近的区域,而Isomap算法却可以将具有相似特征的光谱投影到邻近区域,而将具有不同特征的光谱投影到相距较远的区域;(2)Isomap算法给出的大部分离群点较易判断,且是具有很高科学价值的双星;而PCA给出的离群点难以判断,科学价值不高。因此,在光谱离群点发掘上Isomap算法比PCA有明显优势。由于使用的数据为SDSS最新发布的M型的九种光谱次型的光谱,因而Isomap算法能够快速发现被斯隆数字巡天数据处理流程(SDSS pipeline)错分的光谱,可帮助有效提高现有光谱分类算法的准确率。更进一步,由于被SDSS pipeline错分的光谱大部分是双星,因而Isomap算法还可以进一步帮助我们发现有很高科学研究价值的双星,提高双星的发现效率。虽然实验显示Isomap算法对信噪比变化较为敏感,在具有较低信噪比的光谱上表现较差,但由于信噪比低的光谱的光谱型难以判断,因而该缺点并不影响Isomap算法的在光谱发掘上的应用。     

稀疏正则化最小类散度半监督分类机

基于稀疏表示理论提出一种稀疏正则化最小类散度半监督分类机(SRMCV), 且对于模式分类问题, SRMCV通过引入稀疏Laplacian正则化项和类内散度信息以实现预测空间函数在全局稀疏表示结构下平滑变化, 同时通过类内数据散度结构进一步优化决策函数的判别方向, 此方法能解决现有SSL方法对模型参数敏感和在噪声学习环境下缺乏鲁棒性等问题, 其有效性已在实际数据集上通过实验验证.     

Mesh deformation based on artificial neural networks

In the article a new mesh deformation algorithm based on artificial neural networks is introduced. This method is a point-to-point method, meaning that it does not use connectivity information for calculation of the mesh deformation. Two already known point-to-point methods, based on interpolation techniques, are also presented. In contrast to the two known interpolation methods, the new method does not require a summation over all boundary nodes for one displacement calculation. The consequence of this fact is a shorter computational time of mesh deformation, which is proven by different deformation tests. The quality of the deformed meshes with all three deformation methods was also compared. Finally, the generated and the deformed three-dimensional meshes were used in the computational fluid dynamics numerical analysis of a Francis water turbine. A comparison of the analysis results was made to prove the applicability of the new method in every day computation.     

基于精英协同的混洗差分进化算法及其应用

提出了基于精英协同的混洗差分进化算法(Shuffled Differential Evolution,SDE)。该算法引入反向学习的初始化机制,并对设置的普通群和虚拟精英群采用不同的差分策略,进而将精英个体作为信息通道实现种群间的信息交流;同时,借助定期混洗机制实现种群间的文化交流,从而达到协同进化的目的;此外,对长期停滞的个体进行跳变操作,以充分挖掘种群的搜索潜能,增强搜索的有效性。通过函数仿真,并与PSO及其它差分进化算法比较,结果表明该算法具有较好的寻优能力。