Taba: A Tool to Analyze the Binding Affinity

Evaluation of ligand-binding affinity using the atomic coordinates of a protein-ligand complex is a challenge from the computational point of view. The availability of crystallographic structures of complexes with binding affinity data opens the possibility to create machine-learning models targeted to a specific protein system. Here, we describe a new methodology that combines a mass-spring system approach with supervised machine-learning techniques to predict the binding affinity of protein-ligand complexes. The combination of these techniques allows exploring the scoring function space, generating a model targeted to a protein system of interest. The new model shows superior predictive performance when compared with classical scoring functions implemented in the programs Molegro Virtual Docker, AutoDock4, and AutoDock Vina. We implemented this methodology in a new program named Taba. Taba is implemented in Python and available to download under the GNU license at https://github.com/azevedolab/taba . © 2019 Wiley Periodicals, Inc.     

Emission Control by Molecular Manipulation of Double‐Paddled Binuclear PtII Complexes at the Air‐Water Interface

Molecular functions depend on conformations and motions of the corresponding molecular species. An air–water interface is a suitable asymmetric field for the control of molecular conformations and motions under a small applied force. In this work, double‐paddled binuclear Pt^II complexes containing pyrazole rings linked by alkyl spacers were synthesized and their orientations and emission properties dynamically manipulated at the air–water interface. The complexes emerge from water with concurrent variation of interface orientation of the planes of the Pt^II complexes from perpendicular to parallel during mechanical compression suggesting a unique ‘submarine emission‘. Phosphorescence of the complexes is quenched at the air–water interface prior to monolayer formation with intensities subsequently rapidly increasing during monolayer compression. These results indicate that asymmetric reactions and motions might be controlled by applying mechanical force at the air–water interface.     

排序支持的交互数据分类算法及其应用

交互分类是解决数据复杂分类问题的主要手段之一。在现有的大多交互分类系统中,用户能准确识别数据类别,但在有些分类场景中,类别之间的顺序关系更容易被识别,为此,提出一种排序支持的交互数据分类算法。为提升交互分类精度,引入数据的顺序信息,为降低标记难度,提出候选样本推荐策略。另外,提出一种评估分类算法性能的可视化方法,用包含基本车况、交通违法记录、交通事故记录等信息的车辆数据集进行实验验证,将相关车辆分为高危车辆、中危车辆、低危车辆3类,算法的分类结果模型一致度达近98%,验证了方法的有效性。     

游客情感计算的文本大数据挖掘方法比较研究

旅游文本大数据以其方便、快捷和低门槛的特点为游客情感计算提供了极大便利,已经成为旅游大数据的主要来源之一。基于大数据理论和情感理论,以文本大数据为数据源,在全面梳理国内外情感计算相关成果的基础上,利用人工智能中的逻辑/算法编程方法、机器学习方法、深度学习方法对旅游文本大数据进行挖掘,探索最佳的基于文本大数据的游客情感计算方法。研究发现：（1）基于情感词典的游客情感计算模型,其核心是构建情感词典和设计情感计算规则,方法简单,容易实现,适用语料范围广。（2）机器学习,用统计学方法抽取文本中的特征项,具有非线性特征,可靠性较线性特征的情感词典方法高。（3）基于深度学习技术的游客情感计算,效果良好,准确率在85%以上。训练多领域的文本语料易于移植,实用性强,且泛化能力好,较适合大数据时代游客情感计算研究。     

基于支持向量机的肤色滤波器

为了探测图像中的肤色像素,提出了一种新的方法-支持向量机（SVM：Support Vector Machine）方法.它是一种基于肤色的非特定人的面部定位方法,是非接触人机交互技术和机器视觉中的一个重要内容.实验结果表明,采用支持向量机方法较传统人工神经网络方法不仅有更高的探测准确性,而且具有更好的推广性能.由于SVM采用结构风险最小化（SRM：Structural Risk Minimization）准则,在最小化训练误差（经验风险）的同时,尽量缩小模型预测误差的上界,从而使模型有更好的泛化能力.     

A survey on HHL algorithm: From theory to application in quantum machine learning

The Harrow-Hassidim-Lloyd (HHL) algorithm is a method to solve the quantum linear system of equations that may be found at the core of various scientific applications and quantum machine learning models including the linear regression, support vector machines and recommender systems etc. After reviewing the necessary background on elementary quantum algorithms, we provide detailed account of how HHL is exploited in different quantum machine learning (QML) models, and how it provides the desired quantum speedup in all these models. At the end, we briefly discuss some of the remaining challenges ahead for HHL-based QML models and related methods.     

基于SVM的固化土无侧限抗压强度模型

为实现较少试验次数下固化土无侧限抗压强度(qu)的准确预测, 提出了基于支持向量机(SVM)的固化土qu的预测模型. 以固化剂各组分掺入比、龄期、初始含水量、固化剂掺量等因素为输入量, 固化土的qu作为输出量, 以径向基为核函数, 采用网格搜索法和交叉验证法进行参数优化, 建立了基于SVM的固化土qu的预测模型. 算例分析表明: 该模型适用于任意条件下固化土qu的精确预测, 且在较小试验成本下实现与响应面法相当的预测精度.     

基于工作电流分析的回转副摩擦状态识别实验研究

回转副摩擦状态的变化对机械设备的运行有着重要影响. 针对回转副摩擦状态的识别问题, 设计了以回转副工作电流为摩擦特征来源的实验方案. 通过采集摩擦过程的回转副驱动电机工作电流信号, 分析实验信号的频域和时频域特征, 建立工作电流的摩擦特征库, 用于训练多分类支持向量机分类器, 并在分类器上完成摩擦状态的识别. 实验结果表明, 经特征提取与降维处理后建立的多分类支持向量机分类器具有近90%的识别正确率, 所提出的实验方案有利于实现远距离信号的采集, 适用实际生产加工.     

高精度半导体激光打标机F-θ镜头设计

为保证半导体激光打标机F-θ镜头的扫描质量,实现系统像高与扫描角的线性变化,需对F-θ镜头给予一定的畸变量,并使其满足等晕条件。分析F-θ镜头工作原理及像差要求,根据1 064 nm半导体激光打标机的光源成像要求选择合适的玻璃材料,合理分配每片透镜的光焦度,以保证等晕成像;根据F-θ镜头线性成像要求,计算系统总畸变量为1.6%,系统总畸变量为系统的实际桶形畸变与相对畸变量之和;在光学系统优化设计时,引入这两项优化参数,优化过程中观察系统成像变化情况。设计结果表明:系统MTF曲线接近衍射极限,F-θ镜头相对畸变小于0.36%,各视场均方根半径均小于艾里斑直径,并且整个系统70%的能量集中在直径为16 μm的圆内,系统总畸变量为1.58%,满足设计指标要求。