Measuring Lineup Difficulty By Matching Distance Metrics With Subject Choices in Crowd-Sourced Data

Graphics play a crucial role in statistical analysis and data mining. Being able to quantify structure in data that is visible in plots, and how people read the structure from plots is an ongoing challenge. The lineup protocol provides a formal framework for data plots, making inference possible. The data plot is treated like a test statistic, and lineup protocol acts like a comparison with the sampling distribution of the nulls. This article describes metrics for describing structure in data plots and evaluates them in relation to the choices that human readers made during several large Amazon Turk studies using lineups. The metrics that were more specific to the plot types tended to better match subject choices, than generic metrics. The process that we followed to evaluate metrics will be useful for general development of numerically measuring structure in plots, and also in future experiments on lineups for choosing blocks of pictures. Supplementary materials for this article are available online.     

可变摆长的单摆运动的MATLAB仿真

文章对可变摆长的单摆运动进行了详细的理论分析和推导,并根据不同的摆长将运动分成八种情况。     

Hyperspectral Imaging in the Medical Field: Present and Future

Abstract

Hyperspectral imaging is an optical method that provides a large amount of information about the investigated object. Its medical applications are reviewed in this article, including tumor delimitation and identification, assessing tissue perfusion and its pathological conditions (including some complications like diabetic foot ulceration), making accurate surgical decisions, evaluating the health of dental structures, etc. Many of the articles show very promising results that required brief comments by the authors. It is clear that choosing the appropriate hyperspectral imaging system for each medical field, together with the most reliable hyperspectral image processing methods, are the main goals of future studies, before hyperspectral imaging becomes a widely applicable evaluation method in medicine. The authors try to answer some questions on this topic and set up some directions for future research.     

Estimating and Visualizing Conditional Densities

Abstract

We consider the kernel estimator of conditional density and derive its asymptotic bias, variance, and mean-square error. Optimal bandwidths (with respect to integrated mean-square error) are found and it is shown that the convergence rate of the density estimator is order n ^–2/3. We also note that the conditional mean function obtained from the estimator is equivalent to a kernel smoother. Given the undesirable bias properties of kernel smoothers, we seek a modified conditional density estimator that has mean equivalent to some other nonparametric regression smoother with better bias properties. It is also shown that our modified estimator has smaller mean square error than the standard estimator in some commonly occurring situations. Finally, three graphical methods for visualizing conditional density estimators are discussed and applied to a data set consisting of maximum daily temperatures in Melbourne, Australia.     

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

This article presents individual conditional expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. Classical partial dependence plots (PDPs) help visualize the average partial relationship between the predicted response and one or more features. In the presence of substantial interaction effects, the partial response relationship can be heterogeneous. Thus, an average curve, such as the PDP, can obfuscate the complexity of the modeled relationship. Accordingly, ICE plots refine the PDP by graphing the functional relationship between the predicted response and the feature for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate, suggesting where and to what extent heterogeneities might exist. In addition to providing a plotting suite for exploratory analysis, we include a visual test for additive structure in the data-generating model. Through simulated examples and real datasets, we demonstrate how ICE plots can shed light on estimated models in ways PDPs cannot. Procedures outlined are available in the R package ICEbox.     

The algorithmic beauty of plant roots – an L-System model for dynamic root growth simulation

Understanding the impact of root architecture on plant resource efficiency is important, in particular, in the light of upcoming shortages of mineral fertilizers and changed environmental conditions. In the 1950s, a great number of root systems of European cultivated plants were excavated and studied by L. Kutschera (1960). Her work gave enormous insight into the variety of root system architectures and helped to realize the importance of belowground processes to plant productivity. We analysed the resulting hand drawings by using mathematical modelling and found root system parameters for a newly developed parametric L-System model. In this way we were able to first reproduce the illustrations, second computationally analyse root system traits and finally access the dynamic root architecture development.     

Flow separation behind a rib in a channel with laminar flow

Visualization data and results of combined measurements of flow quantities in flow with separation past a rib at nominally laminar regime of channel flow are reported. In the separation region, the flow is found to be essentially three-dimensional and unsteady, exhibiting a distinct cellular structure and flow zones with transverse motion. It is shown that the rib-induced flow separation gives rise to low-frequency fluctuations of flow velocity and initiates the turbulence transition in the channel flow. The critical Reynolds number at which flow instability starts developing in the channel is estimated. It is shown that at Reynolds numbers higher than the critical Reynolds number the linear integral scale of flow velocity fluctuations in the channel is defined by the duct size.     

In Situ Monitoring of RAFT Polymerization by Tetraphenylethylene‐Containing Agents with Aggregation‐Induced Emission Characteristics

A facile and efficient approach is demonstrated to visualize the polymerization in situ. A group of tetraphenylethylene (TPE)‐containing dithiocarbamates were synthesized and screened as agents for reversible addition fragmentation chain transfer (RAFT) polymerizations. The spatial‐temporal control characteristics of photochemistry enabled the RAFT polymerizations to be ON and OFF on demand under alternating visible light irradiation. The emission of TPE is sensitive to the local viscosity change owing to its aggregation‐induced emission characteristic. Quantitative information could be easily acquired by the naked eye without destroying the reaction system. Furthermore, the versatility of such a technique was well demonstrated by 12 different polymerization systems. The present approach thus demonstrated a powerful platform for understanding the controlled living radical polymerization process.     

多移动机器人的目标跟踪研究

本文主要对低温真空球罐三维可视化温度在线监测系统进行介绍,在真空球罐外罐内壁、内罐外壁、夹层、垂直支架等部位安装温度传感器,通过LabVIEW数据采集板卡实时采集温度信号,在Pro－E中建立球罐三维模型,划分网格后导入到LabVIEW环境中,将采集到的温度信号通过空间插值算法映射到模型中,直观显示真空球罐的整体温度信息,同时,通过LabVIEW三维模型场景对象属性节点和方法节点开发了模型隐藏、剖分、透视以及拾取传感器点等多样化功能,该套在线监测系统已经投入使用,整体性能表现优异,尤其是直观新颖的三维可视化操作界面受到操作人员的一致好评。     

基于GPU的可视化测量仪器软件设计

提出利用显卡图形处理单元(Graphics Processing Units, GPU)的并行信息处理能力解决仪器软件在执行海量数据处理、建模、渲染以及交互所面临的开销过大的难题,基于DirectX 11的计算着色器(Compute Shader,CS)实现海量测量数据的处理和建模以及高速推送渲染,建立在GPU内实现海量数据模型上点的拾取模块,以提高仪器可视化测量中的交互执行。实验比对证实了基于GPU的可视化测量仪器软件的高执行效率。研究为挖掘可视化测量仪器硬件能力、合理配置仪器CPU与GPU开销、在整体上提高仪器运行效率提供了一条有价值的技术路径。