Linklaters-Emerging-Opportunity-Index_YUM_case_stufy
Data Mining常用词汇表-中英文
1R 算法名称Activation function 激励函数Adaptive classifier combination (ACC) 自适应分类器组合Adaptive 自适应Additive 可累加的Affinity analysis 亲和力分析Affinity 亲和力Agglomerative clustering 凝聚聚类Agglomerative 凝聚的Aggregate proximity relationship 整体接近关系Aggregate proximity 整体接近Aggregation hierarchy 聚合层次AGNES 算法名称AIRMA 集成的自回归移动平均Algorithm 算法Alleles 等位基因Alternative hypothesis 备择假设Approximation 近似Apriori 算法名称AprioriAll 算法名称Apriori-Gen 算法名称ARGen 算法名称ARMA 自回归移动平均Artificial intelligence (AI) 人工智能Artificial neural networks (ANN) 人工神经网络Association rule problem 关联规则问题Association rule/ Association rules 关联规则Association 关联Attribute-oriented induction 面向属性的归纳Authoritative 权威的Authority权威Autocorrelation coefficient 自相关系数Autocorrelation 自相关Autoregression 自回归Auto-regressive integrated moving average 集成的自回归移动平均Average link 平均连接Average 平均Averagelink 平均连接Backlink 后向链接back-percolation 回滤Backpropagation 反向传播backward crawling 后向爬行backward traversal 后向访问BANG 算法名称Batch gradient descent 批量梯度下降Batch 批量的Bayes Rule 贝叶斯规则Bayes Theorem 贝叶斯定理Bayes 贝叶斯Bayesian classification 贝叶斯分类BEA 算法名称Bias 偏差Binary search tree 二叉搜索树Bipolar activation function 双极激励函数Bipolar 双极BIRCH 算法名称Bitmap index 位图索引Bivariate regression 二元回归Bond Energy algorithm 能量约束算法Boosting 提升Border point 边界点Box plot 箱线图Boyer-Moore (BM) 算法名称Broker 代理b-tree b树B-tree B-树C4.5 算法名称C5 算法名称C5.0 算法名称CACTUS 算法名称Calendric association rule 日历关联规则Candidate 候选CARMA 算法名称CART 算法名称Categorical 类别的CCPD 算法名称CDA 算法名称Cell 单元格Center 中心Centroid 质心CF tree 聚类特征树CHAID 算法名称CHAMELEON 算法名称Characterization 特征化Chi squared automatic interaction detection 卡方自动交互检测Chi squared statistic 卡方统计量Chi squared 卡方Children 孩子Chromosome 染色体CLARA 算法名称CLARANS 算法名称Class 类别Classification and regression trees 分类和回归树Classification rule 分类规则Classification tree 分类树Classification 分类Click 点击Clickstream 点击流Clique 团Cluster mean 聚类均值Clustering feature 聚类特征Clustering problem 聚类问题，备选定义Clustering 聚类Clusters 簇Collaborative filtering 协同过滤Combination of multiple classifiers (CMC) 多分类器组合Competitive layer 竞争层Complete link 全连接Compression 压缩Concept hierarchy 概念层次Concept 概念Confidence interval 质心区间Confidence 置信度Confusion matrix 混淆矩阵Connected component 连通分量Contains 包含Context Focused Crawler (CFC) 上下文专用爬虫Context graph 上下文图Context layer 上下文层Contiguous subsequeace 邻接子序列Contingency table 列联表Continuous data 连续型数据Convex hull 凸包Conviction 信任度Core points 核心点Correlation coefficient r 相关系数rCorrelation pattern 相关模式Correlation rule 相关规则Correlation 相关Correlogram 相关图Cosine 余弦Count distribution 计数分配Covariance 协方差Covering 覆盖Crawler 爬虫CRH 算法名称Cross 交叉Crossover 杂交CURE 算法名称Customer-sequence 客户序列Cyclic association rules 循环关联规则Data bubbles 数据气泡Data distribution 数据分配Data mart 数据集市Data Mining Query Language (DMQL) 数据挖掘查询语言Data mining 数据挖掘Data model 数据模型Data parallelism 数据并行Data scrubbing 数据清洗Data staging 数据升级Data warehouse 数据仓库Database (DB) 数据库Database Management System 数据库管理系统Database segmentation 数据库分割DBCLASD 算法名称DBMS 算法名称DBSCAN 算法名称DDA算法名称，数据分配算法Decision support systems (DSS) 决策支持系统Decision tree build 决策树构造Decision tree induction 决策树归纳Decision tree model (DT model) 决策树模型Decision tree processing 决策树处理Decision tree 决策树Decision trees 决策树Delta rule delta规则DENCLUE 算法名称Dendrogram 谱系图Density-reachable 密度可达的Descriptive model 描述型模型Diameter 直径DIANA 算法名称dice 切块Dice 一种相似度量标准Dimension modeling 维数据建模Dimension table 维表Dimension 维Dimensionality curse 维数灾难Dimensionality reduction 维数约简Dimensions 维Directed acyclic graph (DAG) 有向无环图Direction relationship 方向关系Directly density-reachable 直接密度可达的Discordancy test 不一致性测试Dissimilarity measure 差别度量Dissimilarity 差别Distance based simple 基于距离简单的Distance measure 距离度量Distance scan 距离扫描Distance 距离Distiller 提取器Distributed 分布式的Division 分割Divisive clustering 分裂聚类Divisive 分裂的DMA 算法名称Domain 值域Downward closed 向下封闭的Drill down 下钻Dynamic classifier selection (DCS) 动态分类器选择EM 期望最大值Encompassing circle 包含圆Entity 实体Entity-relationship data model 实体-关系数据模型Entropy 熵Episode rule 情节规则Eps-neighborhood ϵ-邻域Equivalence classes 等价类Equivalent 等价的ER data model ER数据模型ER diagram ER图Euclidean distance 欧几里得距离Euclidean 欧几里得Evaluation 评价Event sequence 事件序列Evolutionary computing 进化计算Executive information systems (EIS) 主管信息系统Executive support systems (ESS) 主管支持系统Exhaustive CHAID 穷尽CHAIDExpanded dimension table 扩张的维表Expectation-maximization 期望最大化Exploratory data analysis 探索性数据分析Extensible Markup Language 可扩展置标语言Extrinsic 外部的Fact table 事实表Fact 事实Fallout 错检率False negative (FN) 假反例False positive (FP) 假正例Farthest neighbor 最远邻居FEATUREMINE 算法名称Feedback 反馈Feedforward 前馈Finite state machine (FSM) 有限状态机Finite state recognizer 有限状态机识别器Firefly 算法名称Fires 点火Firing rule 点火规则Fitness function 适应度函数Fitness 适应度Flattened dimension table 扁平的维表Flattened 扁平的Focused crawler 专用爬虫Forecasting 预报Forward references 前向访问Frequency distribution 频率分布Frequent itemset 频率项目集Frequent 频率的Fuzzy association rule 模糊关联规则Fuzzy logic 模糊逻辑Fuzzy set 模糊集GA clustering 遗传算法聚类Gain 增益GainRatio 增益比率Gatherer 收集器Gaussian activation function 高斯激励函数Gaussian 高斯GDBSCAN 算法名称Gene 基因Generalization 泛化，一般化Generalized association rules 泛化关联规则Generalized suffix tree (GST) 一般化的后缀树Generate rules 生成规则Generating rules from DT 从决策树生成规则Generating rules from NN 从神经网络生成规则Generating rules 生成规则Generic algorithms 遗传算法Genetic algorithm 遗传算法Genetic algorithms (GA) 遗传算法Geographic Information Systems (GIS) 地理信息系统Gini 吉尼Gradient descent 梯度下降g-sequence g-序列GSP 一般化的序列模式Hard focus 硬聚焦Harvest rate 收获率Harvest 一个Web内容挖掘系统Hash tree 哈希树Heapify 建堆Hebb rule hebb规则Hebbian learning hebb学习Hidden layer 隐层Hidden Markov Model 隐马尔可夫模型Hidden node 隐节点Hierarchical classifier 层次分类器Hierarchical clustering 层次聚类Hierarchical 层次的High dimensionality 高维度Histogram 直方图HITS 算法名称Hmm 隐马尔可夫模型HNC Risk Suite 算法名称HPA 算法名称Hub 中心Hybrid Distribution (HD) 混合分布Hybrid OLAP (HOLAP) 混合型联机分析处理Hyper Text Markup Language 超文本置标语言Hyperbolic tangent activation function 双曲正切激励函数Hyperbolic tangent 双曲正切Hypothesis testing 假设检验ID3 算法名称IDD 算法名称Inverse document frequency(IDF) 文档频率倒数Image databases 图像数据库Incremental crawler 增量爬虫Incremental gradient descent 增量梯度下降Incremental rules 增量规则Incremental updating 增量更新Incremental 增量的Individual 个体Induction 归纳Information gain 信息增益Information retrieval (IR) 信息检索Information 信息Informational data 情报数据Input layer 输入层Input node 输入节点Integration 集成Interconnections 相互连接Interest 兴趣度Interpretation 解释Inter-transaction association rules 事务间关联规则Inter-transaction 事务之间Intra-transaction association rules 事务内关联规则Intra-transaction 事务之内Intrinsic 内部的Introduction 引言IR 算法名称Isothetic rectangle isothetic矩形Issues 问题Itemset 项目集Iterative 迭代的JaccardJaccard’s coefficient Jaccard系数Jackknife estimate 折叠刀估计Java Data Mining (JDM) Java数据挖掘Join index 连接索引K nearest neighbors (KNN) K最近邻K-D tree K-D树KDD object KDD对象KDD process KDD过程Key 键K-means K-均值K-Medoids K-中心点K-Modes K-模KMP 算法名称Knowledge and data discovery management system (KDDMS) 知识与数据发现管理系统Knowledge discovery in databases (KDD) 数据库知识发现Knowledge discovery in spatial databases 空间数据库知识发现Knuth-Morris-Pratt algorithm 算法名称Kohonen self organizing map Kohonen自组织映射k-sequence K-序列Lag 时滞Large itemset property 大项集性质Large itemset 大项集Large reference sequence 强访问序列Large sequence property 大序列性质Large 大Learning parameter 学习参数Learning rule 学习准则Learning 学习Learning-rate 学习率Least squares estimates 最小二乘估计Levelized dimension table 层次化维表Lift 作用度Likelihood 似然Linear activation function 线性激励函数Linear discriminant analysis (LDA) 线性判别分析Linear filter 线性滤波器Linear regression 线性回归Linear 线性Linear 线性的Link analysis 连接分析Location 位置LogisticLogistic regression logistic回归Longest common subseries 最长公共子序列Machine learning 机器学习Major table 主表Manhattan distance 曼哈顿距离Manhattan 曼哈顿Map overlay 地图覆盖Market basket analysis 购物篮分析Market basket 购物篮Markov Model (MM) 马尔可夫模型Markov Property 马尔可夫性质Maximal flequent forward sequences 最长前向访问序列Maximal forward reference 最长前向访问Maximal reference sequences 最长访问序列Maximum likelihood estimate (MLE) 极大似然估计MBR 最小边界矩形Mean squared error (MSE) 均方误差Mean squared 均方Mean 均值Median 中值Medoid 中心点Merged context graph 合并上下文图Method of least squares 最小二乘法Metric 度量Minimum bounded rectangle 最小边界矩阵Minimum item supports 最小项目支持度Minimum Spanning Tree algorithm 最小生成树算法Minimum Spanning Tree (MST) 最小生成树Minor table 副表MinPts 输入参数名称MINT 一种网络查询语言MISapriori 算法名称Mismatch 失配Missing data 缺失数据Mode 模Momentum 动量Monothetic 单一的Moving average 移动平均Multidimensional Database (MDD) 多维数据库Multidimensional OLAP (MOLAP) 多维OLAP Multilayer perceptron (MLP) 多层感知器Multimedia data 多媒体数据Multiple Layered DataBase (MLDB) 多层数据库Multiple linear regression 多元线性回归Multiple-level association rules 多层关联规则Mutation 变异Naïve Bayes 朴素贝叶斯Nearest hit 同类最近Nearest miss 异类最近Nearest Neighbor algorithm 最近邻算法Nearest neighbor query 最近邻查询Nearest neighbor 最近邻Nearest Neighbors 最近邻Negative border 负边界Neighborhood graph 近邻图Neighborhood 邻居Neural network (NN) 神经网络Neural network model (NN model) 神经网络模型Neural networks 神经网络Noise 噪声Noisy data 噪声数据Noncompetitive learning 非竞争性学习Nonhierarchical 非层次的Nonlinear regression 非线性回归Nonlinear 非线性的Nonparametric model 非参数模型Nonspatial data dominant generalization 以非空间数据为主的一般化Nonspatial hierarchy 非空间层次Nonstationary 非平稳的Normalized dimension table 归一化维表NSD CLARANS 算法名称Null hypothesis 空假设OAT 算法名称Observation probability 观测概率OC curve OC曲线Ockham’s razor 奥卡姆剃刀Offline gradient descent 离线梯度下降Offline 离线Offspring 子孙OLAP 联机分析处理Online Analytic Processing 在线梯度下降Online gradient descent 在线梯度下降Online transaction processing (OLTP) 联机事务处理Online 在线Operational characteristic curve 操作特征曲线Operational data 操作型数据OPTICS 算法名称OPUS 算法名称Outlier detection 异常点检测Outlier 异常点Output layer 输出层Output node 输出结点Overfitting 过拟合Overlap 重叠Page 页面PageRank 算法名称PAM 算法名称Parallel algorithms 并行算法Parallel 并行的Parallelization 并行化Parametric model 参数模型Parents 双亲Partial-completeness 部分完备性Partition 分区Partitional clustering 基于划分的聚类Partitional MST 划分MST算法Partitional 划分的Partitioning Around Medoids 围绕中心点的划分Partitioning 划分Path completion 路径补全Pattern detection 模式检测Pattern discovery 模式发现Pattern matching 模式匹配Pattern Query Language (PQL) 模式查询语言Pattern recognition 模式识别Pattern 模式PDM 算法名称Pearson’s r 皮尔逊系数rPerceptron 感知器Performance measures 性能度量Performance 性能Periodic crawler 周期性爬虫Personalization 个性化Point estimation 点估计PolyAnalyst 附录APolythetic 多的Population 种群Posterior probability 后验概率Potentially large 潜在大的Precision 查准率Predicate set 谓词集合Prediction 预测Predictive model 预测型模型Predictive Modeling Mark-Up Language (PMML) 预测模型置标语言Predictor 预测变量Prefix 前缀Preprocessing 预处理Prior probability 先验概率PRISM 算法名称Privacy 隐私Processing element function 处理单元函数Processing elements 处理单元Profile association rule (PAR) 简档关联规则Profiling 描绘Progressive refinement 渐进求精Propagation 传播Pruning 剪枝Quad tree 4叉树Quantitative association rule 数量关联规则Quartiles 4分位树Query language 查询语言Querying 查询QUEST 算法名称R correlation coefficient r相关系数Radial basis function (RBF) 径向基函数Radial function 径向函数Radius 半径RainForest 算法名称Range query 范围查询Range 全距Rank sink 排序沉没Rare item problem 稀疏项目问题Raster 光栅Ratio rule 比率规则RBF network 径向基函数网络Recall 召回率Receiver operating characteristic curve 接受者操作特征曲线Recurrent neural network 递归神经网络Referral analysis 推荐分析Region query 区域查询Regression coefficients 回归稀疏Regression 回归Regressor 回归变量Related concepts 相关概念Relation 关系Relational algebra 关系代数Relational calculus 关系计算Relational model 关系模型Relational OLAP 关系OLAPRelationship 关系Relative operating characteristic curve 相对操作特征曲线Relevance 相关性Relevant 相关的Reproduction 复制Response 响应Return on investment 投资回报率RMSE 均方根误差RNN 递归神经网络Robot 机器人ROC curve ROC曲线ROCK algorithm ROCK算法ROI 投资回报率ROLAP 关系型联机分析处理Roll up 上卷Root mean square error 均方根误差Root mean square (Rms) 均方根Roulette wheel selection 轮盘赌选择R-tree R树Rule extraction 规则抽取Rules 规则Sampling 抽样SAND 算法名称Satisfy 满足Scalability 可伸缩性Scalable parallelizable induction of decision trees 决策树的可伸缩并行归纳Scatter diagram 散点图Schema 模式SD CLARANS 算法名称，空间主导的Search engine 搜索引擎Search 搜索Seed URL 种子URLSegmentation 分割Segments 片段Selection 选择Self organizing feature map (SOFM) 自组织特征映射Self organizing map (SOM) 自组织映射Self organizing neural networks 自组织神经网络Self organizing 自组织Semantic index 语义索引Sequence association rule problem 序列关联规则问题Sequence association rule 序列关联规则Sequence association rules 序列关联规则Sequence classifier 序列分类器Sequence discovery 序列发现Sequence 时间序列Sequential analysis 序列分析Sequential pattern 序列模式Sequential patterns 序列模式Serial 单行的Session 会话Set 集合SGML 一种置标语言Shock 冲击Sigmoid activation function S型激励函数Sigmoid S型的Silhouette coefficient 轮廓系数Similarity measure 相似性度量Similarity measures 相似性度量Similarity 相似性Simple distance based 简单基于距离的Simultaneous 同时的Single link 单连接Slice 切片Sliding window 滑动窗口SLIQ 算法名称Smoothing 平滑Snapshot 快照Snowflake schema 雪花模式Soft focus 软聚焦SPADE 算法名称Spatial Association Rule 空间关联规则Spatial association rules 空间关联规则Spatial characteristic rules 空间特征曲线Spatial clustering 空间聚类Spatial data dominant generalization 以空间数据为主的一般化Spatial data mining 空间数据挖掘Spatial data 空间数据Spatial database 空间数据库Spatial Decision Tree 空间决策树Spatial discriminant rule 空间数据判别规则Spatial hierarchy 空间数据层次Spatial join 空间连接Spatial mining 空间数据挖掘Spatial operator 空间运算符Spatial selection 空间选择Spatial-data-dominant 空间数据主导Spider 蜘蛛Splitting attributes 分裂属性Splitting predicates 分裂谓语Splitting 分裂SPRINT 算法名称SQL 结构化查询语言Squared Error algorithm 平方误差算法Squared error 平方误差Squashing function 压缩函数Standard deviation 标准差Star schema 星型模式Stationary 平稳的Statistical inference 统计推断Statistical significance 统计显著性Statistics 统计学Step activation function 阶跃激励函数Step 阶跃Sting build 算法名称STING 算法名称Strength 强度String to String Conversion 串到串转换Subepisode 子情节Subsequence 子序列Subseries 子序列Subtree raising 子树上升Subtree replacement 子树替代Suffix tree 后缀树Summarization 汇总Supervised learning 有指导的学习Support 支持度SurfAid Analytics 附录ASurprise 惊奇度Targeting 瞄准Task parallelism 任务并行Temporal association rules 时序关联规则Temporal database 时序数据库Temporal mining 时序数据挖掘Temporal 时序Term frequency (TF) 词频Thematic map 主题地图Threshold activation function 阈值激励函数Threshold 阈值Time constraint 时间约束Time line 大事记Time series analysis 时间序列分析Time series 时间序列Topological relationship 拓扑关系Training data 训练数据Transaction time 事务时间Transaction 事务Transformation 变换Transition probability 转移概率Traversal patterns 浏览模式Trend dependency 趋势依赖Trend detection 趋势检测Trie 一种数据结构True negative (TN) 真反例True positive (TP) 真正例Unbiased 无偏的Unipolar activation function 单极激励函数Unipolar 单级的Unsupervised learning 无指导学习Valid time 有效时间Variance 方差Vector 向量Vertical fragment 纵向片段Virtual warehouse 虚拟数据仓库Virtual Web View (VWV) 虚拟Web视图Visualization 可视化V oronoi diagram V oronoi图V oronoi polyhedron V oronoi多面体WAP-tree WAP树WaveCluster 算法名称Wavelet transform 小波变换Web access patterns Web访问模式Web content mining Web内容挖掘Web log Web日志Web mining Web挖掘Web usage mining Web使用挖掘Web Watcher 一种方法WebML 一种Web挖掘查询语言White noise 白噪声WordNet Semantic NetworkWordNet 一个英语词汇数据库。
m-p模型及函数关系 -回复
m-p模型及函数关系-回复MP模型（也称为乘数效应模型）是经济学中用于描述经济波动和政策效应的一种模型，它是由约翰·梅纳德·凯恩斯（John Maynard Keynes）在20世纪30年代提出的。
MP模型和函数关系是指MP模型中的各个组成部分（如消费、投资、政府支出和净出口）与宏观经济运行中的函数关系。
以下将逐步探讨MP模型及其与函数关系的相关性。
首先，我们需要了解MP模型的基本构成。
MP模型由几个关键的宏观经济变量组成，包括GDP（国内生产总值）、消费、投资、政府支出和净出口。
其中，GDP是衡量一个国家经济活动总量的指标，消费是人们购买商品和服务的支出，投资是企业购买资本设备和厂房等的支出，政府支出是政府为提供公共服务和刺激经济增长而进行的支出，净出口是一个国家与其他国家之间的商品和服务贸易的差额。
接下来，我们来探讨MP模型中各个组成部分与宏观经济运行中的函数关系。
首先是消费和GDP之间的关系。
根据凯恩斯的理论，消费是GDP的一个重要驱动因素。
消费函数描述了消费者在不同收入水平下的消费行为。
一般情况下，随着收入的增加，消费也会增加，但增加的速度会逐渐减缓，这就是所谓的边际消费倾向。
其次是投资和GDP之间的关系。
投资函数描述了企业在不同经济环境下进行投资的行为。
投资受到许多因素的影响，如预期经济增长率、利率、技术进步等。
当经济增长预期高、利率低且技术进步迅速时，企业更愿意进行投资，从而促进GDP的增长。
再次是政府支出和GDP之间的关系。
政府支出函数描述了政府在不同时间和政策环境下进行支出的行为。
政府支出往往用于提供公共服务和刺激经济增长。
当政府增加支出时，会刺激需求，从而促进GDP的增长。
最后是净出口和GDP之间的关系。
净出口函数描述了一个国家与其他国家之间的贸易差额。
如果一个国家的出口多于进口，就会产生净出口的正数差额，从而促进GDP的增长。
相反，如果一个国家的进口多于出口，就会产生净出口的负数差额，从而抑制GDP的增长。
美国标准化机构参与海外PPP项目的案例研究
国际前沿美国标准化机构参与海外PPP项目的案例研究■ 刘 昕1 王笑微2（1. 中国电机工程学会；2. 西安热工研究院有限公司）摘 要：政府与社会资本合作（PPP）是一种结合了公共部门与私营部门双方优势的融资模式。
美国政府通过PPP模式与非营利性的民间标准化机构ANSI开展合作，资助后者在海外开展美国标准及技术的宣传推广活动。
在发展中国家开展新兴市场发掘与扶持，往往也意味着可为本国经济带来新的增长点。
灵活且高效的标准化机构在进行技术价值输出与铺垫的同时，在新兴市场成型早期影响其标准化上下游体系机制的路线选择。
这一政府与民间标准化机构的合作模式，从长远看对本国技术产业在海外开拓新兴市场，并且在市场发展的早期阶段占据先发优势，进而获得长远竞争优势具有重要战略价值，对于我国在“一带一路”等双多边国际经济合作中如何更好地开展标准化活动具有借鉴意义。
关键词：政府与社会资本合作（PPP），标准化体系，民间标准化机构，发展中国家，“一带一路”DOI编码：10.3969/j.issn.1002-5944.2021.17.037A Case Study of Overseas Public-Private Partnership Projects Participatedby American Standardization InstitutesLIU Xin1 WANG Xiao-wei2（1. Chinese Society for Electrical Engineering; 2. Xi’an Thermal Power Research Institute Co., Ltd.）Abstract: Public-private partnership (PPP) is a financing model that combines the advantages of the public and private sectors. The U.S. government cooperates with the non-governmental standardization organization ANSI through the PPP model, and subsidizes the latter to carry out promotion activities for American standards and technologies overseas. The incubation and support of emerging markets in developing countries often implies that the new growth driver of the country's economy. Conducted by the flexible and efficient standardization organizations, the standards activities also pave the way for tech export, influencing the route selection of both upstream and downstream in the standardization system at the early stage of emerging markets. The cooperation model between the government and the private standardization institute has important strategic value for the domestic technology industry to explore emerging markets overseas, and to gain first-mover advantage in the early stage of market development, and to gain long-term competitive advantages. A closer look at the overseas promotion of domestic standards through PPP model is of important strategic value for China’s standardization activities in bilateral and multilateral international economic cooperation such as the Belt and Road Initiative.Keywords: public-private partnership, standardization system, non-governmental standardization institute, developing countries, Belt and Road Initiative基金项目：本文受中国华能集团有限公司软科学研究年度计划项目（项目编号：2020 ZD-6）资助。
【毕业论文】创业机会的认知与开发
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这七个创新机会的来源之间并没有清晰的界限，而 且有重叠的地方。这种情形可以被看作是一栋建筑物 的七个窗户，每个窗户看到的部分景色均可从另一扇 窗户中看到，但从窗户正中得到的景象却截然不同。
这七个来源需要分别进行分析，每一个都有自己明 显的特点，不能说哪一个更为重要或更富于成果。重 大的创新往往来自对各种变化的分析（例如意想不到 的成功常常是由不起眼的产品或价格变动引起的）或 来自一项伟大的科学突破所带来的新知识的广泛运用。
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❖ [美]管理大师彼得.德鲁克（P.F.Drucher）在《创 业精神与创新——变革时代的管理原则与实践》一书 中提出：
创新是创业家所特有的工具，他们借助这个工具把 各种变化开拓成为从事不同事业或服务的机会。创新 是一种知识，是可以学以致用的。创业家需要刻意探 索创新的来源，寻找那些孕育着创新的变化。
利用意想不到的成功提供的机会去创新需要分析。意 想不到的成功是一种征兆，但究竟是什么征兆？阻碍我 们深入分析的要害，莫过于我们自己受自己的眼光、知 识和理解力的局限。
世界上两家最大的公司，杜邦——最大的化学公司和 IBM——计算机产业的巨子，其卓著的成绩应归功于他 们202主4/8/动6 地把意想不到的成功作为创新机会来开发。 10
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三、 创业机会的认知
（一）机会之窗
一个具体的创业机会，其存在的时间是短暂的。 Timmons在他的著作里描述了一般化市场上的“机会 之窗”。一个市场在不同时间阶段，其成长的速度是 不同的。在市场快速发展的阶段，创业的机会随之增 多；发展到一定阶段，形成一定结构后，机会之窗打 开；市场发展成熟之后，机会之窗就开始关闭。选择 那些机会之窗存在的时间长一些的市场机会，创业企 业可获利的时间也可长一些，取得成功的概率就大一 些。这样的机会，其期望价值自然高一些。
半监督学习综述ppt文档资料课件
划分视图这一策略并非总能奏效，
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Figure: Co-Training: Conditional independent assumption on feature split. With this assumption the high confident data points in x1 view, represented by circled labels, will be randomly scattered in x2 view. This is advantageous if they are to be used to teach the classifier in x2 view.
他们 又对该算法进行了扩展，使其能够使用多个不 同种类的分类器。
tri-training算法：不仅可以简便地处理标记置信度估 计问题以及对未见示例的预测问题，还可以利用集成 学习（ensemble learning）来提高泛化能力
这类问题直接来自于实际应用：例如，大量医学影 像，医生把每张片子上的每个病灶都标出来再进行 学习，是不可能的，能否只标一部分，并且还能利 用未标的部分？
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资金是运动的价值，资金的价值是随 时间变 化而变 化的， 是时间 的函数 ，随时 间的推 移而增 值，其 增值的 这部分 资金就 是原有 资金的 时间价 值
半监督学习应用实例
语音识别（Speech recognition） 文本分类（Text categorization） 词义解析（Parsing） 视频监控（Video surveillance） 蛋白质结构预测（Protein structure
prediction）
模拟ai英文面试题目及答案
模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。
triplot 1.3.0 用户手册说明书
Package‘triplot’October14,2022Title Explaining Correlated Features in Machine Learning ModelsVersion1.3.0Description Tools for exploring effects of correlated features in predictive models.The predict_triplot()function delivers instance-level explanationsthat calculate the importance of the groups of explanatory variables.Themodel_triplot()function delivers data-level explanations.The generic plotfunction visualises in a concise way importance of hierarchical groups ofpredictors.All of the the tools are model agnostic,therefore works for anypredictive machine learning models.Find more details in Biecek(2018)<arXiv:1806.08915>.Depends R(>=3.6)License GPL-3Encoding UTF-8LazyData trueRoxygenNote7.1.1Imports ggplot2,DALEX(>=1.3),glmnet,ggdendro,patchworkSuggests testthat,knitr,randomForest,mlbench,ranger,gbm,covrURL https:///ModelOriented/triplotBugReports https:///ModelOriented/triplot/issues Language en-USNeedsCompilation noAuthor Katarzyna Pekala[aut,cre],Przemyslaw Biecek[aut](<https:///0000-0001-8423-1823>) Maintainer Katarzyna Pekala<**************************>Repository CRANDate/Publication2020-07-1317:00:03UTC1R topics documented:aspect_importance (2)aspect_importance_single (4)calculate_triplot (6)cluster_variables (8)get_sample (9)group_variables (10)hierarchical_importance (10)list_variables (12)plot.aspect_importance (13)plot.cluster_variables (14)plot.triplot (15)print.aspect_importance (16)Index18aspect_importance Calculates importance of variable groups(called aspects)for a se-lected observationDescriptionPredict aspects function takes a sample from a given dataset and modifies it.Modification is made by replacing part of its aspects by values from the observation.Then function is calculating the difference between the prediction made on modified sample and the original sample.Finally,it measures the impact of aspects on the change of prediction by using the linear model or lasso.Usageaspect_importance(x,...)##S3method for class explaineraspect_importance(x,new_observation,variable_groups,N=1000,n_var=0,sample_method="default",f=2,...)##Default S3method:aspect_importance(x,data,predict_function=predict,label=class(x)[1],new_observation,variable_groups,N=100,n_var=0,sample_method="default",f=2,...)lime(x,...)predict_aspects(x,...)Argumentsx an explainer created with the DALEX::explain()function or a model to be ex-plained....other parametersnew_observationselected observation with columns that corresponds to variables used in themodelvariable_groupslist containing grouping of features into aspectsN number of observations to be sampled(with replacement)from data NOTE: Small N may cause unstable results.n_var maximum number of non-zero coefficients after lassofitting,if zero than linear regression is usedsample_method sampling method in get_samplef frequency in get_sampledata dataset,it will be extracted from x if it’s an explainer NOTE:It is best when target variable is not present in the datapredict_functionpredict function,it will be extracted from x if it’s an explainer label name of the model.By default it’s extracted from the’class’attribute of the model.ValueAn object of the class aspect_importance.Contains data frame that describes aspects’impor-tance.Exampleslibrary("DALEX")model_titanic_glm<-glm(survived==1~class+gender+age+sibsp+parch+fare+embarked,data=titanic_imputed,family="binomial")explain_titanic_glm<-explain(model_titanic_glm,data=titanic_imputed[,-8],y=titanic_imputed$survived==1,verbose=FALSE)aspects<-list(wealth=c("class","fare"),family=c("sibsp","parch"),personal=c("gender","age"),embarked="embarked")predict_aspects(explain_titanic_glm,new_observation=titanic_imputed[1,],variable_groups=aspects)library("randomForest")library("DALEX")model_titanic_rf<-randomForest(factor(survived)~class+gender+age+sibsp+parch+fare+embarked,data=titanic_imputed)explain_titanic_rf<-explain(model_titanic_rf,data=titanic_imputed[,-8],y=titanic_imputed$survived==1,verbose=FALSE)predict_aspects(explain_titanic_rf,new_observation=titanic_imputed[1,],variable_groups=aspects)aspect_importance_singleAspects importance for single aspectsDescriptionCalculates aspect_importance for single aspects(every aspect contains only one feature). Usageaspect_importance_single(x,...)##S3method for class explaineraspect_importance_single(x,new_observation,N=1000,n_var=0,sample_method="default",f=2,...)##Default S3method:aspect_importance_single(x,data,predict_function=predict,label=class(x)[1],new_observation,N=1000,n_var=0,sample_method="default",f=2,...)Argumentsx an explainer created with the DALEX::explain()function or a model to be ex-plained....other parametersnew_observationselected observation with columns that corresponds to variables used in themodel,should be without target variableN number of observations to be sampled(with replacement)from data NOTE: Small N may cause unstable results.n_var how many non-zero coefficients for lassofitting,if zero than linear regression is usedsample_method sampling method in get_samplef frequency in in get_sampledata dataset,it will be extracted from x if it’s an explainer NOTE:Target variable shouldn’t be present in the datapredict_functionpredict function,it will be extracted from x if it’s an explainer label name of the model.By default it’s extracted from the’class’attribute of the model.ValueAn object of the class’aspect_importance’.Contains dataframe that describes aspects’importance.Exampleslibrary("DALEX")model_titanic_glm<-glm(survived==1~class+gender+age+sibsp+parch+fare+embarked,data=titanic_imputed,family="binomial")explainer_titanic<-explain(model_titanic_glm,data=titanic_imputed[,-8],verbose=FALSE)aspect_importance_single(explainer_titanic,new_observation=titanic_imputed[1,-8])calculate_triplot Calculate triplot that sums up automatic aspect/feature importancegroupingDescriptionThis function shows:•plot for the importance of single variables,•tree that shows importance for every newly expanded group of variables,•clustering tree.Usagecalculate_triplot(x,...)##S3method for class explainercalculate_triplot(x,type=c("predict","model"),new_observation=NULL,N=1000,loss_function=DALEX::loss_root_mean_square,B=10,fi_type=c("raw","ratio","difference"),clust_method="complete",cor_method="spearman",...)##Default S3method:calculate_triplot(x,data,y=NULL,predict_function=predict,label=class(x)[1],type=c("predict","model"),new_observation=NULL,N=1000,loss_function=DALEX::loss_root_mean_square,B=10,fi_type=c("raw","ratio","difference"),clust_method="complete",cor_method="spearman",...)##S3method for class triplotprint(x,...)model_triplot(x,...)predict_triplot(x,...)Argumentsx an explainer created with the DALEX::explain()function or a model to be ex-plained....other parameterstype if predict then aspect_importance is used,if model than feature_importance iscalculatednew_observationselected observation with columns that corresponds to variables used in themodel,should be without target variableN number of rows to be sampled from data NOTE:Small N may cause unstableresults.loss_function a function that will be used to assess variable importance,if type=modelB integer,number of permutation rounds to perform on each variable in featureimportance calculation,if type=modelfi_type character,type of transformation that should be applied for dropout loss,if type=model."raw"results raw drop losses,"ratio"returns drop_loss/drop_loss_full_model.clust_method the agglomeration method to be used,see hclust methodscor_method the correlation method to be used see cor methodsdata dataset,it will be extracted from x if it’s an explainer NOTE:Target variableshouldn’t be present in the data8cluster_variables y true labels for data,will be extracted from x if it’s an explainerpredict_functionpredict function,it will be extracted from x if it’s an explainer label name of the model.By default it’s extracted from the’class’attribute of the model.Valuetriplot objectExampleslibrary(DALEX)set.seed(123)apartments_num<-apartments[,unlist(lapply(apartments,is.numeric))]apartments_num_lm_model<-lm(m2.price~.,data=apartments_num)apartments_num_new_observation<-apartments_num[30,]explainer_apartments<-explain(model=apartments_num_lm_model,data=apartments_num[,-1],y=apartments_num[,1],verbose=FALSE)apartments_tri<-calculate_triplot(x=explainer_apartments,new_observation=apartments_num_new_observation[-1]) apartments_tricluster_variables Creates a cluster tree from numeric featuresDescriptionCreates a cluster tree from numeric features and their correlations.Usagecluster_variables(x,...)##Default S3method:cluster_variables(x,clust_method="complete",cor_method="spearman",...) Argumentsx dataframe with only numeric columns...other parametersclust_method the agglomeration method to be used see hclust methodscor_method the correlation method to be used see cor methodsget_sample9 Valuean hclust objectExampleslibrary("DALEX")dragons_data<-dragons[,c(2,3,4,7,8)]cluster_variables(dragons_data,clust_method="complete")get_sample Function for getting binary matrixDescriptionFunction creates binary matrix,to be used in aspect_importance method.It starts with a zero matrix.Then it replaces some zeros with ones.If sample_method="default"it randomly replaces one or two zeros per row.If sample_method="binom"it replaces random number of zeros per row-average number of replaced zeros can be controlled by parameter sample_method="f".Function doesn’t allow the returned matrix to have rows with only zeros.Usageget_sample(n,p,sample_method=c("default","binom"),f=2)Argumentsn number of rowsp number of columnssample_method sampling methodf frequency for binomial samplingValuea binary matrixExamplesget_sample(100,6,"binom",3)10hierarchical_importancegroup_variables Helper function that combines clustering variables and creating as-pect listDescriptionDivides correlated features into groups,called aspects.Division is based on correlation cutoff level. Usagegroup_variables(x,h,clust_method="complete",cor_method="spearman") Argumentsx hclust objecth correlation value for tree cuttingclust_method the agglomeration method to be used see hclust methodscor_method the correlation method to be used see cor methodsValuelist with aspectExampleslibrary("DALEX")dragons_data<-dragons[,c(2,3,4,7,8)]group_variables(dragons_data,h=0.5,clust_method="complete")hierarchical_importanceCalculates importance of hierarchically grouped aspectsDescriptionThis function creates a tree that shows order of feature grouping and calculates importance of every newly created aspect.hierarchical_importance11Usagehierarchical_importance(x,data,y=NULL,predict_function=predict,type="predict",new_observation=NULL,N=1000,loss_function=DALEX::loss_root_mean_square,B=10,fi_type=c("raw","ratio","difference"),clust_method="complete",cor_method="spearman",...)##S3method for class hierarchical_importanceplot(x,absolute_value=FALSE,show_labels=TRUE,add_last_group=TRUE,axis_lab_size=10,text_size=3,...)Argumentsx a model to be explained.data dataset NOTE:Target variable shouldn’t be present in the datay true labels for datapredict_functionpredict functiontype if predict then aspect_importance is used,if model than feature_importance is calculatednew_observationselected observation with columns that corresponds to variables used in themodel,should be without target variableN number of rows to be sampled from data NOTE:Small N may cause unstable results.loss_function a function that will be used to assess variable importance,if type=modelB integer,number of permutation rounds to perform on each variable in featureimportance calculation,if type=model12list_variables fi_type character,type of transformation that should be applied for dropout loss,if type=model."raw"results raw drop losses,"ratio"returns drop_loss/drop_loss_full_model.clust_method the agglomeration method to be used,see hclust methodscor_method the correlation method to be used see cor methods...other parametersabsolute_value if TRUE,aspects importance values will be drawn as absolute valuesshow_labels if TRUE,plot will have annotated axis Yadd_last_group if TRUE,plot will draw connecting line between last two groupsaxis_lab_size size of labels on axis Y,if applicabletext_size size of labels annotating values of aspects importanceValueggplotExampleslibrary(DALEX)apartments_num<-apartments[,unlist(lapply(apartments,is.numeric))]apartments_num_lm_model<-lm(m2.price~.,data=apartments_num)hi<-hierarchical_importance(x=apartments_num_lm_model,data=apartments_num[,-1],y=apartments_num[,1],type="model")plot(hi,add_last_group=TRUE,absolute_value=TRUE)list_variables Cuts tree at custom height and returns a listDescriptionThis function creates aspect list after cutting a cluster tree of features at a given height.Usagelist_variables(x,h)Argumentsx hclust objecth correlation value for tree cuttingValuelist of aspectsplot.aspect_importance13Exampleslibrary("DALEX")dragons_data<-dragons[,c(2,3,4,7,8)]cv<-cluster_variables(dragons_data,clust_method="complete")list_variables(cv,h=0.5)plot.aspect_importanceFunction for plotting aspect_importance resultsDescriptionThis function plots the results of aspect_importance.Usage##S3method for class aspect_importanceplot(x,...,bar_width=10,show_features=aspects_on_axis,aspects_on_axis=TRUE,add_importance=FALSE,digits_to_round=2,text_size=3)Argumentsx object of aspect_importance class...other parametersbar_width bar widthshow_features if TRUE,labels on axis Y show aspect names,otherwise they show features namesaspects_on_axisalias for show_features held for backwards compatibility add_importance if TRUE,plot is annotated with values of aspects importancedigits_to_roundinteger indicating the number of decimal places used for rounding values ofaspects importance shown on the plottext_size size of labels annotating values of aspects importance,if applicable14plot.cluster_variablesValuea ggplot2objectExampleslibrary("DALEX")model_titanic_glm<-glm(survived==1~class+gender+age+sibsp+parch+fare+embarked,data=titanic_imputed,family="binomial")explain_titanic_glm<-explain(model_titanic_glm,data=titanic_imputed[,-8],y=titanic_imputed$survived==1,verbose=FALSE)aspects<-list(wealth=c("class","fare"),family=c("sibsp","parch"),personal=c("gender","age"),embarked="embarked")titanic_ai<-predict_aspects(explain_titanic_glm,new_observation=titanic_imputed[1,],variable_groups=aspects)plot(titanic_ai)plot.cluster_variablesPlots tree with correlation valuesDescriptionPlots tree that illustrates the results of cluster_variables function.Usage##S3method for class cluster_variablesplot(x,p=NULL,show_labels=TRUE,axis_lab_size=10,text_size=3,...) Argumentsx cluster_variables or hclust objectp correlation value for cutoff level,if not NULL,cutoff line will be drawnshow_labels if TRUE,plot will have annotated axis Yaxis_lab_size size of labels on axis Y,if applicabletext_size size of labels annotating values of correlations...other parametersplot.triplot15ValueplotExampleslibrary("DALEX")dragons_data<-dragons[,c(2,3,4,7,8)]cv<-cluster_variables(dragons_data,clust_method="complete")plot(cv,p=0.7)plot.triplot Plots triplotDescriptionPlots triplot that sum up automatic aspect/feature importance groupingUsage##S3method for class triplotplot(x,absolute_value=FALSE,add_importance_labels=FALSE,show_model_label=FALSE,abbrev_labels=0,add_last_group=TRUE,axis_lab_size=10,text_size=3,bar_width=5,margin_mid=0.3,...)Argumentsx triplot objectabsolute_value if TRUE,aspect importance values will be drawn as absolute valuesadd_importance_labelsif TRUE,first plot is annotated with values of aspects importance on the bars show_model_labelif TRUE,adds subtitle with model labelabbrev_labels if greater than0,labels for axis Y in single aspect importance plot will be ab-breviated according to this parameteradd_last_group if TRUE and type=predict,plot will draw connecting line between last two groups at the level of105biggest importance value,for model this line is alwaysdrawn at the baseline valueaxis_lab_size size of labels on axistext_size size of labels annotating values of aspects importance and correlationsbar_width bar width in thefirst plotmargin_mid size of a right margin of a middle plot...other parametersValueplotExampleslibrary(DALEX)set.seed(123)apartments_num<-apartments[,unlist(lapply(apartments,is.numeric))]apartments_num_lm_model<-lm(m2.price~.,data=apartments_num)apartments_num_new_observation<-apartments_num[30,]explainer_apartments<-explain(model=apartments_num_lm_model,data=apartments_num[,-1],y=apartments_num[,1],verbose=FALSE)apartments_tri<-calculate_triplot(x=explainer_apartments,new_observation=apartments_num_new_observation[-1])plot(apartments_tri)print.aspect_importanceFunction for printing aspect_importance resultsDescriptionThis function prints the results of aspect_importance.Usage##S3method for class aspect_importanceprint(x,show_features=FALSE,show_corr=FALSE,...)Argumentsx object of aspect_importance classshow_features show list of features for every aspectshow_corr show if all features in aspect are pairwise positively correlated(for numeric features only)...other parametersExampleslibrary("DALEX")model_titanic_glm<-glm(survived==1~class+gender+age+sibsp+parch+fare+embarked,data=titanic_imputed,family="binomial")explain_titanic_glm<-explain(model_titanic_glm,data=titanic_imputed[,-8],y=titanic_imputed$survived==1,verbose=FALSE)aspects<-list(wealth=c("class","fare"),family=c("sibsp","parch"),personal=c("gender","age"),embarked="embarked")titanic_ai<-predict_aspects(explain_titanic_glm,new_observation=titanic_imputed[1,],variable_groups=aspects)print(titanic_ai)Indexaspect_importance,2aspect_importance_single,4calculate_triplot,6cluster_variables,8cor,7,8,10,12get_sample,3,5,9group_variables,10hclust,7,8,10,12hierarchical_importance,10lime(aspect_importance),2list_variables,12model_triplot(calculate_triplot),6plot.aspect_importance,13plot.cluster_variables,14plot.hierarchical_importance(hierarchical_importance),10 plot.triplot,15predict_aspects(aspect_importance),2 predict_triplot(calculate_triplot),6 print.aspect_importance,16print.triplot(calculate_triplot),618。
改进的leslie模型在流动人口较大城市的应用——以深圳为例
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2024北京高三一模英语汇编：阅读理解D篇
2024北京高三一模英语汇编阅读理解D篇一、阅读理解（2024北京门头沟高三一模）A recent global study, which surveyed 10,000 young people from 10 countries, showed that nearly 60 percent of them were extremely worried about the future state of the planet. The report, which was published in The Lancet, also showed that nearly half of the respondents said that such distress affected them daily, and three quarters agreed with the statement that “the future is frightening.” This, along with many other studies, shows clearly that climate change is not just a threat to the environment that we inhabit. It also poses a very real threat to our emotional well-being. Psychologists have categorized these feelings of grief and worry about the current climate emergency, a common occurrence among youth today, under the label of “eco-anxiety”.Eco-anxiety doesn’t just affect young people. It also affects researchers who work in climate and ecological science, burdened by the reality depicted by their findings, and it affects the most economically marginalized (边缘化的) across the globe, who bear the damaging impacts of climate breakdown.In 2024, eco-anxiety will rise to become one of the leading causes of mental health problems. The reasons are obvious. Scientists estimate that the world is likely to breach safe limits of temperature rise above pre-industrial levels for the first time by 2027.In recent years, we’ve seen wildfires tear through Canada and Greece, and summer floods ruin regions in Pakistan that are home to nearly 33 million people. Studies have shown that those impacted by air pollution and rising temperatures are more likely to experience psychological distress.To make matters worse, facing climate crisis, our political class is not offering strong leadership. The COP28 conference in Dubai will be headed by an oil and gas company executive. In the UK, the government is backtracking on its green commitments.Fortunately, greater levels of will also offer an avenue for resolving the climate crisis directly. According to Caroline Hickman, a researcher on eco-anxiety from the University of Bath, anyone experiencing eco-anxiety is displaying entirely natural and rational reactions to the climate crisis. This is why, in 2024, we will also see more people around the world join the fight for climate justice and seek jobs that prioritize environmental sustainability. Campaigners will put increased pressure on fossil fuel industries and the governments to rapidly abandon the usage of polluting coal, oil, and gas.It’s now clear that not only are these industries the main causes for the climate crisis, they are also responsible for the mental health crisis, which is starting to affect most of us. Eco-anxiety is not something we will defeat with therapy, but something we will tackle by taking action.1．What can we learn from the passage?A．The cause of eco-anxiety is emotions existing in our mind.B．People in developed countries are more likely to suffer from eco-anxiety.C．Eco-anxiety is a new kind of psychological disease due to climate change.D．The author is disappointed about government behaviour towards climate crisis.2．What does the underlined word “breach” in Paragraph 3 most probably mean?A．Break.B．Reach.C．Raise.D．Affect.3．As for Caroline Hickman’s opinion on eco-anxiety, the author is .A．puzzled B．favourable C．suspicious D．unconcerned4．What would be the best title for the passage?A．Who Is to Blame for Eco-anxiety?B．How Should You See Eco-anxiety?C．How Will Eco-anxiety Be Resolved?D．Why Do People Suffer from Eco-anxiety?（2024北京延庆高三一模）It is rapidly emerging as one of the most important technological, and increasingly ideological, divides of our times: should powerful generative artificial intelligence systems be open or closed?Supporters say they broaden access to the technology, stimulate innovation and improve reliability by encouraging outside scrutiny. Far cheaper to develop and deploy, smaller open models also inject competition into a field dominated by big US companies such as Google. Microsoft and OpenAI that have invested billions developing massive, closed and closely controlled generative Al systems.But detractors argue open models risk lifting the lid on a Pandora’s box of troubles. Bad actors can exploit them to spread personalised disinformation, while terrorists might use them to manufacture cyber or bio weapons. “The danger of open source is that it enables more crazies to do crazy things, “Geoffrey Hinton, one of the pioneers of modern AI, has warned.The history of OpenAI, which developed the popular ChatGPT chatbot, is itself instructive. As its name suggests, the research company was founded in 2015 with a commitment to develop the technology as openly as possible. But it later abandoned that approach for both competitive and safety reasons. Once OpenAI realised that its generative AI models were going to be “unbelievably potent”, it made little sense to open source them, Ilya Sutskever, OpenAI’s chief scientist said.Supporters of open models hit back, ridiculing the idea that open generative AI models enable people to access information they could not otherwise find from the internet or a rogue scientist. They also highlight the competitive self-interest of the big tech companies in shouting about the dangers of open models, whose intention is to establish their own market dominance strongly.But there is an ideological dimension to this debate, too. Yann LeCun, chief scientist of Meta, has likened the arguments for controlling the technology to medieval obscurantism (蒙昧主义): the belief that only a self-selecting priesthood of experts is wise enough to handle knowledge.In the future, all our interactions with the vast digital repository of human knowledge will be mediated through Al systems. We should not want a handful of Silicon Valley companies to control that access. Just as the internet flourished by resisting attempts to enclose it, so AI will thrive by remaining open, LeCun argues.Wendy Hall, royal professor of computer science at Southampton university, says we do not want to live in a world where only the big companies run generative Al. Nor do we want to allow users to do anything they like with open models. “We have to find some compromise,” she suggests.We should certainly resist the tyranny (暴政) of the binary (二进制) when it comes to thinking about AI models. Both open and closed models have their benefits and flaws. As the capabilities of these models evolve, we will constantly have to tweak the weightings between competition and control.5. What does the underlined word “potent” in Paragraph 4 most probably mean?A. Accessible.B. Powerful.C. Significant.D. Unnoticeable.6. What can we learn from this passage?A. It needs billions of dollars to develop and deploy open-source models.B. The field of generative AI systems is dominated by big companies.C. Only self-selecting experts can handle open models wisely.D. Users can do anything they like with open models at this moment.7. Regarding Wendy Hall’s suggestions, the author is ______.A. sympatheticB. puzzledC. unconcernedD. opposed8. Which of the following would be the best title for the passage?A. How to Keep the Lid on the Pandora’s Box of Open AIB. Divides on Open AI: technology and ideologyC. Where does the Debate on Open AI EndD. Pros and Cons of Open AI（2024北京东城高三一模）When I teach research methods, a major focus is peer review. As a process, peer review evaluates academic papers for their quality, integrity and impact on a field, largely shaping what scientists accept as "knowledge"- By instinct, any academic follows up a new idea with the question, "Was that peer reviewed?"Although I believe in the importance of peer review and I help do peer reviews for several academic journals-I know how vulnerable the process can be.I had my first encounter with peer review during my first year as a Ph. D student. One day, my adviser handed me an essay and told me to have my -written review back to him in a week. But at the time, I certainly was not a "peer"--I was too new in my field. Manipulated data （不实的数据）or substandard methods could easily have gone undetected. Knowledge is not self-evident. Only experts would be able to notice them, and even then, experts do not always agree on what they notice.Let's say in my life I only see white swans. Maybe I write an essay, concluding that all swans are white. And a "peer" says, "Wait a minute, I've seen black swans. "I would have to refine my knowledge.The peer plays a key role evaluating observations with the overall goal of advancing knowledge. For example, if the above story were reversed, and peer reviewers who all believed that all swans were white came across the first study observing a black swan, the study would receive a lot of attention.So why was a first-year graduate student getting to stand in for an expert? Why would my review count the same as an expert's review? One answer: The process relies almost entirely on unpaid labor.Despite the fact that peers are professionals, peer review is not a profession. As a result, the same over-worked scholars often receive masses of the peer review requests. Besides the labor inequity, a small pool of experts can lead to a narrowed process of what is publishable or what counts as knowledge, directly threatening diversity of perspectives and scholars. Without a large enough reviewer pool, the process can easily fall victim to biases, arising from a small community recognizing each other's work and compromising conflicts of interest.Despite these challenges. I still tell my students that peer review offers the best method for evaluating studies aird advancing knowledge. As a process, peer review theoretically works. The question is whether the issues with peer review can be addressed by professionalizing the field.9. What can we learn about peer review in the first paragraph?A. It generates knowledge.B. It is commonly practiced.C. It is a major research method.D. It is questioned by some scientists.10. What can be inferred about the example of swans?A. Complexity of peer review ensures its reliability.B. Contradictions between scientists may be balanced.C. Individuals can be limited by personal experiences.D. Experts should detect unscientific observation methods.11. What is the author's major concern about peer review?A. Workload for scholars.B. Toughness of the process.C. Diversification of publications.D. Financial support to reviewers.12. The passage is mainly about ______.A. what fuels peer review B why peer review is imperfectC. how new hands advance peer reviewD. whether peer reviewers are underrated（2024北京西城高三一模）While some allergies（过敏症）disappear over time or with treatment, others last a lifetime. For decades, scientists have been searching for the source of these lifetime allergies.Recently, researchers found that memory B cells may be involved. These cells produce a different class of antibodies known as IgG, which ward off viral infections But no one had identified exactly which of those cells were recalling allergens or how they switched to making the IgE antibodies responsible for allergies. To uncover the mysterious cells, two research teams took a deep dive into the immune （免疫的）cells of people with allergies and some without.Immunologist Joshua Koenig and colleagues examined more than 90, 000 memory B cells from six people with birch allergies, four people allergic to dust mites and five people with no allergies. Using a technique called RNA sequencing. the team identified specific memory B cells. which they named MBC2s. that make antibodies and proteins associated with the immune response that causes allergiesIn another experiment, Koenig and colleagues used a peanut protein to go fishing for memory B cells from people with peanut allergies. The team pulled out the same type of cells found in people with birch and dust mite allergies. In people with peanut allergies, those cells increased in number and produced IgE antibodies as the people started treatment to desensitize them to peanut allergens.Another group led by Maria Curotto de Lafaille, an immunologist at the Icahn School of Medicine at Mount Sinai in New York City, also found that similar cells were more. plentiful in 58 children allergic to peanuts than in 13 kids without allergies. The team found that the cells are ready to switch from making protective IgG antibodies to allergy-causing IgE antibodies. Even before the switch, the cells were making RNA for IgE but didn't produce the protein. Making that RNA enables the cells to switch the type of antibodies they make when they encounter allergens. The signal to switch partially depends on a protein called JAK. the group discovered. "Stopping JAK from sending the signal could help prevent the memory cells from switching to IgE production, " Lafaille says. She also predicts that allergists may be able to examine aspects of these memory cells to forecast whether a patient's allergy is likely to last or disappear with time or treatment.“Knowing which population of cells store allergies in long-term memory may eventually help scientists identify other ways to kill the allergy cells, " says Cecilia Berin, an immunologist at Northwestern University Feinberg School of Medicine. "You could potentially get rid of not only your peanut allergy but also all of your allergies. "13. Why did scientists investigate the immune cells of individuals with and without allergies?A. To explore the distinctions between IgG and IgE.B. To uncover new antibodies known as IgG and IgE.C. To identify cells responsible for defending against allergies.D. To reveal cells associated with the development of allergies.14. What does the word "desensitize" underlined in Paragraph 4 most probably mean?A. Make. . . less destructive.B. Make. . . less responsive.C. Make. . . less protective.D. Make. . . less effective.15. What can we learn from the two research teams' work?A. MBC2s make antibodies and proteins that prevent allergies.B. Memory B cells generate both RNA for IgE and the corresponding protein.C. JAK plays a role in controlling antibody production when exposed to allergens.D. Allergists are capable of predicting whether an allergy will last or disappear.16. Which could be the best title for the passage?A. RNA Sequencing Is Applied in Immunology ResearchB. Specific Cells Related to Peanut Allergies Are IdentifiedC. Unmasking Cells' Identities Helps Diagnose and Treat AllergiesD. Newfound Immune Cells Are Responsible for Long-lasting Allergies（2024北京石景山高三一模）On Feb.21, four students were standing on the side of Pacific Coast Highway in Malibu when a driver going 110 miles per hour lost control of his car and it crashed into the parked vehicles.12 people were killed at the scene, including 2 drivers.This kind of traffic death shouldn't be called an accident. In Los Angeles, we seem to have accepted constant carnage（屠杀）in our streets in exchange for maximizing driver speed and convenience. The official responses to proven traffic dangers are mere gestures, if even that.Los Angeles is a uniquely deadly city with a death rate that is four times the national average. Unsurprisingly, it's also a city that has been designed with one thing in mind: a concept called level of service, which grades streets on how well they serve those in automobiles. To many Angelenos, that makes sense—to design our streets for car traffic, which is the way many get around the city. Unfortunately, we don't recognize that there's a trade-off. We can either have streets bettered for free-flowing traffic, or we can design streets for people to move around safely outside of cars.City leaders consistently choose for the easy but deadly option. In one recent example, a resident asked the city's Department of Transportation to block drivers from using Cochran Avenue at Venice Boulevard as a cut-through street, as they were speeding through a quiet residential neighbourhood. The department responded by suggesting a "speed awareness campaign" in which neighbours put up yard signs urging drivers to slow down.People don't drive based on signage, but they drive on the design of the street. The trunk roads of Los Angeles such as Venice Boulevard all need to be revised so that people are prioritized over cars. This would include narrowing travel lanes（道）, building bike lanes, and banning right turns at red lights. These measures would make drivers feel like they're in a city and not on a highway. A recent John Hopkins study says this would have substantial safety benefits.With more than 7,500 miles of streets in the city of Los Angeles, they won't all be rebuilt anytime soon. But with each road construction project, or each crash, we should be revising streets to make them safer for all road users.The solution to traffic jam isn't to make more space for cars. It's to design the streets to be safe enough for alternatives such as biking, walking and mass transit, especially for the 50% of trips daily in Los Angeles that are less than three miles. The solution to protecting people dining outdoors isn't crash barriers. It's a street design that forces drivers to go slowly. The problem is carnage in the streets, and we know the solutions.17. Why should the traffic death in Los Angeles be called “constant carnage”？A. The traffic accidents happen quite often.B. Too many people are killed in the traffic accidents.C. The drivers' speeding is to blame for the traffic death.D. City leaders' consistent choice contributes to the traffic death.18. What does the word "trade-off" underlined in Paragraph 3 most probably mean?A. Balance.B. Guideline.C. Conflict.D. Resolution.19. According to the passage, which is a likely solution to the traffic problem?A. To widen travel lanes.B. To add more crosswalks.C. To arrange more traffic police.D. To punish speeding drivers.20. Which would be the best title for the passage?A. Drivers first or walkers first?B. Traffic death or constant carnage?C. More warning signs or safer designs?D. More narrow lanes or speedy highways?（2024北京丰台高三一模）Several dozen graduate students in London were recently tasked with outwitting a large language model (LLM), a type of AI designed to hold useful conversations. LLMs are often programmed with guardrails designed to stop them giving harmful replies: instructions on making bombs in a bathtub, say, or the confident statement of “facts” that are not actually true.The aim of the task was to break those guardrails. Some results were merely stupid. For example, one participant got the chatbot to claim ducks could be used as indicators of air quality. But the most successful efforts were those that made the machine produce the titles, publication dates and host journals of non-existent academic articles.AI has the potential to be a big benefit to science. Optimists talk of machines producing readable summaries of complicated areas of research; tirelessly analysing oceans of data to suggest new drugs and even, one day, coming up with hypotheses of their own. But AI comes with downsides, too.Start with the simplest problem: academic misconduct. Some journals allow researchers to use LLMs to help write papers. But not everybody is willing to admit to it. Sometimes, the fact that LLMs have been used is obvious. Guillaume Cabanac, a computer scientist, has uncovered dozens of papers that contain phrases such as “regenerate response”-the text of a button in some versions of ChatGPT that commands the program to rewrite its most recent answer, probably copied into the manuscript(原稿)by mistake.Another problem arises when AI models are trained on AI-generated data. LLMs are trained on text from the Internet. As they churn out(大量炮制)more such text, the risk of LLMs taking in their own outputs grows. That can cause “model collapse”. In 2023 Ilia Shumailov, a computer scientist, co-authored a paper in which a model wasfed handwritten digits and asked to generate digits of its own, which were fed back to it in turn. After a few cycles, the computer's numbers became more or less illegible. After 20iterations (迭代),it could produce only rough circles or blurry lines.Some worry that computer-generated insights might come from models whose inner workings are not understood. Inexplainable models are not useless, says David Leslie at an AI-research outfit in London, but their outputs will need rigorous testing in the real world. That is perhaps less unnerving than it sounds. Checking models against reality is what science is supposed to be about, after all.For now, at least, questions outnumber answers. The threats that machines pose to the scientific method are, at the end of the day, the same ones posed by humans.AI could accelerate the production of nonsense just as much as it accelerates good science. As the Royal Society has it, nullius in verba: take nobody's word for it. No thing's, either.21.The result of the task conducted in London shows that___________.A. LLMs give away useful informationB. the guardrails turn out to be ineffectiveC.AI's influence will potentially be decreasedD. the effort put into the study of AI hardly pays off22.What does “model collapse”indicate?A. The readability of the models' output is underestimated.B. The diverse sources of information confuse the models.C. Training on regenerated data stops models working well.D. The data will become reliable after continuous iterations.23.According to the passage, people's worry over the inexplainable models is___________.A. impracticalB. unjustifiedC. groundlessD. unsettling24.What would be the best title for the passage?A. Faster Nonsense: AI Could Also Go WrongB. Imperfect Models: How Will AI Make Advances?C. The Rise of LLMs: AI Could Still Be PromisingD. Bigger Threats: AI Will Be Uncontrollable参考答案1．D 2．A 3．B 4．B【导语】这是一篇说明文。
m-p模型及函数关系 -回复
m-p模型及函数关系-回复MP模型（Monetary Policy Model）是一种宏观经济模型，用于解释货币政策与经济变量之间的关系。
通过分析MP模型中的函数关系，可以更好地理解货币政策对经济的影响。
在本文中，将逐步解释MP模型的基本原理、函数关系以及它们之间的关联。
首先，我们来了解一下MP模型的基本原理。
MP模型是一种动态随机一般均衡模型，用于描述一个经济体中的市场交易和货币政策制定的过程。
该模型基于特定的经济假设，包括货币政策的目标、经济体的行为以及市场的运行规则。
尽管MP模型可以较好地解释经济体的运行机制，但它也是一个简化的模型，忽略了一些细节和复杂性。
在MP模型中，有三个主要的函数关系：IS函数、LM函数和AS函数。
IS函数描述了投资和储蓄之间的关系，LM函数描述了货币供应和货币需求之间的关系，AS函数描述了价格水平和产出之间的关系。
这些函数关系在MP模型中相互作用，决定了经济体中的均衡水平和价格水平。
首先，我们来了解IS函数。
IS函数表示投资和储蓄之间的关系，即投资的需求和储蓄的供给。
在MP模型中，IS函数可以表示为：Y = C(Y-T) + I(r) + G其中，Y表示总产出，C(Y-T)表示消费对收入的依赖程度，I(r)表示投资对利率的依赖程度，G表示政府支出，T表示税收。
IS函数表示总产出与这些变量之间的均衡关系，当所有变量达到均衡状态时，总产出也将达到一个稳定的水平。
接下来，我们来了解LM函数。
LM函数表示货币供应与货币需求之间的关系，即货币供应的调节和利率的影响。
在MP模型中，LM函数可以表示为：M/P = L(Y, r)其中，M表示货币供应，P表示价格水平，L(Y, r)表示货币需求的函数，它取决于总产出和利率。
LM函数描述了整个经济体中货币市场的运行情况，当货币供应和货币需求达到均衡时，利率和价格水平将达到一个平衡状态。
最后，我们来了解AS函数。
AS函数表示价格水平和产出之间的关系，即通货膨胀与经济活动之间的关系。
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考研英语二练习题
考研英语二练习题一、阅读理解（共20分）Passage 1In recent years, there has been an increasing trend of people choosing to work remotely. This shift has been driven by advancements in technology, which allow employees to collaborate and complete tasks from anywhere with an internet connection. However, despite the benefits of remote work, such as increased flexibility and reduced commuting time, there are also challenges that need to be addressed.Questions:1. What is the main topic of the passage?2. What are some of the advantages of remote work mentionedin the passage?3. What does the passage suggest about the challenges of remote work?Passage 2The concept of lifelong learning has gained significant importance in today's fast-paced world. With the rapid evolution of technology and the constant emergence of new fields, it is essential for individuals to continuously update their knowledge and skills. This not only helps in personal development but also enhances employability in thejob market.Questions:1. Why is lifelong learning considered important?2. What are the benefits of lifelong learning mentioned in the passage?3. How does lifelong learning impact an individual's career prospects?二、完形填空（共10分）In today's competitive job market, it is crucial for job seekers to stand out. One way to do this is by developing a strong personal brand. Your personal brand is essentially the image you project to the world, which includes your skills, values, and personality traits. By carefully crafting and promoting your personal brand, you can increase yourvisibility and attractiveness to potential employers.[Choose the best word to complete the sentences below.]1. In the context of job seeking, a personal brand can be a powerful tool to __________.A) obscure B) enhance C) diminish D) duplicate2. The image you project should be a reflection of your__________.A) hobbies B) appearance C) values D) interests3. Promoting your personal brand effectively can __________ your chances of employment.A) reduce B) multiply C) eliminate D) stabilize三、翻译（共20分）将以下句子从中文翻译成英文。
一阶马尔可夫链 r语言 -回复
一阶马尔可夫链r语言-回复什么是一阶马尔可夫链?马尔可夫链是由俄国数学家马尔可夫提出的一种随机过程模型。
一阶马尔可夫链是其中最简单的一种形式。
它的基本思想是当前状态只与前一个状态有关，与更早的状态无关。
一阶马尔可夫链广泛应用于各个领域，如自然语言处理、统计学、经济学等，用于建模系统的转移状态和预测未来状态。
如何构建一阶马尔可夫链模型？构建一阶马尔可夫链模型需要两个基本要素：状态集合和状态转移矩阵。
状态集合：所研究的系统可能出现的所有状态的集合。
状态可以是离散的或连续的，取决于具体问题。
例如，对于一个天气预测模型，状态可以是“晴天”、“多云”和“雨天”。
状态转移矩阵：描述状态之间转移的概率。
对于一阶马尔可夫链模型，状态转移矩阵是一个方阵，其中的元素表示从一个状态转移到另一个状态的概率。
下面以一个简单的天气预测模型为例进行一阶马尔可夫链的构建。
假设某地的天气状态只有“晴天”和“雨天”，则状态集合为{晴天, 雨天}。
为了构建状态转移矩阵，我们需根据历史数据统计各个状态之间的转移概率。
假设我们对某地的天气进行了观测，得到了以下历史数据：晴天->晴天->雨天->晴天->晴天->雨天根据这些数据，我们可以计算状态转移概率。
在这个例子中，从“晴天”到“晴天”的转移次数为2，从“晴天”到“雨天”的转移次数为1，因此转移概率为2/3。
同样地，从“雨天”到“晴天”的转移概率为1/3。
将这些概率填入状态转移矩阵中，得到如下表示：晴天雨天晴天2/3 1/3雨天1/3 2/3此即为我们构建的一阶马尔可夫链模型。
如何利用一阶马尔可夫链进行预测？一阶马尔可夫链模型可以用来预测未来状态。
假设我们已经观测到了前几天的天气情况，我们想要预测第四天的天气。
以前三天的天气为例，假设观测数据为：晴天->晴天->雨天。
根据我们构建的状态转移矩阵，我们可以计算在当前观测的情况下，下一步天气的概率。
在这个例子中，当前观测为“晴天->晴天->雨天”，因此我们需要找到“雨天”之后的状态概率。
关于专业介绍的英文采访
关于专业介绍的英文采访Interview on Career Expertise.Interviewer: Welcome, and thank you for joining us today. Could you please introduce yourself and your professional background?Professional: My name is [Professional's Name], and I am a seasoned professional with over [Number] years of experience in [Industry]. Throughout my career, I have held various leadership roles at renowned organizations such as [Organization Name] and [Organization Name], where I have consistently exceeded expectations and driven exceptional results.Interviewer: That's impressive! Can you elaborate on your specific areas of expertise within [Industry]?Professional: My expertise encompasses a wide spectrum of disciplines within [Industry]. I specialize in [List ofAreas]. These areas include [Specific Expertise 1], [Specific Expertise 2], and [Specific Expertise 3]. I possess a deep understanding of [Industry] principles and best practices, enabling me to provide strategic guidance and implement innovative solutions.Interviewer: Could you provide an example of a notable project or achievement that showcases your expertise?Professional: One noteworthy project that I led was [Project Name] at [Organization Name]. This project involved [Project Description]. Through my strategic planning, effective team leadership, and innovative problem-solving skills, I successfully [Project Results]. This project not only exceeded client expectations but also garnered industry recognition.Interviewer: What are the key challenges and trendsthat you have observed in [Industry]?Professional: The [Industry] landscape is constantly evolving, presenting both challenges and opportunities. Onesignificant challenge is [Challenge 1]. To address this, I advocate for [Solution 1]. Another emerging trend is [Trend 1]. This trend has the potential to revolutionize [Industry] by [Impact of Trend]. By embracing these challenges and staying abreast of industry trends, we can fosterinnovation and drive progress.Interviewer: How do you continue to develop your professional skills and knowledge?Professional: Continued professional development is paramount to my success. I actively participate in industry conferences, workshops, and webinars to stay updated on the latest advancements. Additionally, I engage in self-directed learning through books, articles, and online resources. I am also an avid mentor and enjoy sharing my knowledge and experience with aspiring professionals.Interviewer: What advice would you offer to individuals who are aspiring to pursue a career in [Industry]?Professional: To thrive in [Industry], I recommenddeveloping a strong foundation in [Core Skills]. Additionally, building relationships, networking, and seeking out mentors can be invaluable. It is also essential to stay informed about industry trends and embrace innovation. With dedication, hard work, and a passion for [Industry], individuals can achieve great success.Interviewer: Thank you for sharing your insights and expertise. Is there anything else you would like to add?Professional: I would like to emphasize the importance of ethical conduct and integrity in [Industry]. By upholding high ethical standards, we can build trust,foster collaboration, and contribute positively to the industry's reputation. I am committed to maintaining the highest levels of professionalism and integrity in all my endeavors.Interviewer: Thank you once again for your valuable insights. We appreciate your time and expertise.。
位姿回归,场景回归的学术英文表示英语
位姿回归,场景回归的学术英文表示英语## Pose Regression and Scene Regression.Introduction.Pose regression and scene regression are two important tasks in computer vision. Pose regression aims to estimate the pose of an object or a human body from images or videos. Scene regression aims to estimate the scene context of an image or a video, which can include the location, time, and activity.Pose Regression.Pose regression is a challenging task due to the high dimensionality of the pose space and the large variationsin appearance of objects and human bodies. Early approaches to pose regression used hand-crafted features and linear models. However, these approaches were limited in their accuracy and generalization ability.More recently, deep learning has been successfully applied to pose regression. Deep learning models can learn complex features from data and can handle high-dimensional input data. Convolutional neural networks (CNNs) are a particularly popular type of deep learning model for pose regression.CNNs have been used to achieve state-of-the-art results on a variety of pose regression datasets. For example, on the MPII Human Pose dataset, CNNs have achieved an accuracy of over 90%.Scene Regression.Scene regression is a more complex task than pose regression, as it requires understanding the semantics of the scene. Early approaches to scene regression used hand-crafted features and probabilistic models. However, these approaches were limited in their accuracy andgeneralization ability.More recently, deep learning has been successfully applied to scene regression. Deep learning models can learn complex features from data and can handle high-dimensional input data. Convolutional neural networks (CNNs) are a particularly popular type of deep learning model for scene regression.CNNs have been used to achieve state-of-the-art results on a variety of scene regression datasets. For example, on the Places365 dataset, CNNs have achieved an accuracy of over 80%.Applications.Pose regression and scene regression have a wide range of applications, including:Object recognition.Human-computer interaction.Robotics.Autonomous driving.Conclusion.Pose regression and scene regression are two important tasks in computer vision. Deep learning has been successfully applied to both tasks, and has achieved state-of-the-art results on a variety of datasets. These tasks have a wide range of applications, and are likely to continue to be an important area of research in the future.。
rrt_exploration详解 -回复
rrt_exploration详解-回复rrt_exploration详解：一种用于自主探索路径规划的算法引言：路径规划是机器人在未知环境中进行自主探索的基本问题。
Rapidly exploring Random Trees(RRT)是一种常用的路径规划算法，它通过构建随机树来搜索机器人的可行路径。
RRT算法在自主导航、避障和无人驾驶等领域得到广泛应用。
本文将逐步介绍RRT_exploration算法的基本原理和实现过程，包括RRT算法的发展背景、基本原理、扩展版本和应用等方面。
第一部分：RRT算法的发展背景首先，我们先来看一下RRT算法的背景和由来。
RRT算法最早由Steven M. LaValle于1998年提出，旨在解决机器人自主探索路径规划的问题。
该算法是一种基于采样和构建随机树的算法，通过随机选择点并扩展树的枝干，最终找到一个可行路径。
第二部分：RRT算法的基本原理接下来，我们将详细介绍RRT算法的基本原理。
RRT算法主要分为两个步骤：树的扩展和路径的选择。
1.树的扩展：RRT算法从起始点出发，通过在随机方向上扩展树的枝干来搜索可行路径。
具体步骤如下：a.初始化树，将起始点作为树的根节点。
b.根据一定的策略，在树中选择一个节点作为当前节点。
c.在当前节点的周围随机采样一个点。
d.扩展树的枝干，连接当前节点和随机采样的点。
如果连接的路径没有碰撞，将随机采样的点作为新的节点加入树中。
2.路径的选择：RRT算法需要找到从起始点到目标点的可行路径。
具体步骤如下：a.在树中选择离目标点最近的节点作为当前节点。
b.从当前节点出发，通过回溯的方式找到从起始点到当前节点的路径。
第三部分：RRT_exploration算法的扩展在实际应用中，RRT算法也存在一些问题，例如容易陷入局部最优解、不适用于复杂环境等。
为了解决这些问题，人们提出了RRT_exploration算法的扩展版本。
RRT_exploration算法的扩展主要包括以下几个方面：1.路径质量度量：为了使机器人选择更优的路径，RRT_exploration引入了路径质量度量指标，如路径长度、障碍物碰撞次数等。
风险投资案例之斯皮尔曼相关系数
风险投资案例之斯皮尔曼相关系数斯皮尔曼相关系数（Spearman's rank correlation coefficient）是一种用于衡量两个变量之间的关联程度的统计指标。
它的取值范围为-1到1，其中-1表示完全的负相关，1表示完全的正相关，0表示无相关关系。
在风险投资领域，斯皮尔曼相关系数可以用来评估不同投资项目之间的相关性，帮助投资者进行合理的资产配置和风险管理。
下面列举了10个与风险投资案例相关的斯皮尔曼相关系数的应用场景。
1. 评估股票收益率与市场指数之间的相关性：投资者可以使用斯皮尔曼相关系数来衡量不同股票收益率与市场指数之间的相关程度，从而判断股票的系统性风险。
2. 比较不同基金之间的风险：斯皮尔曼相关系数可以用来比较不同基金的风险水平，帮助投资者选择适合自己风险偏好的基金。
3. 评估不同行业之间的相关性：投资者可以使用斯皮尔曼相关系数来衡量不同行业之间的相关程度，从而判断行业间的相互影响和联动程度。
4. 判断投资组合中不同资产之间的相关性：斯皮尔曼相关系数可以用来评估投资组合中不同资产之间的相关性，帮助投资者进行资产配置和风险控制。
5. 评估不同投资策略之间的相关性：投资者可以使用斯皮尔曼相关系数来比较不同投资策略的相关性，从而选择最佳的投资组合。
6. 比较不同投资经理的业绩表现：斯皮尔曼相关系数可以用来比较不同投资经理的业绩表现，从而评估其能力和水平。
7. 评估不同投资指标之间的相关性：斯皮尔曼相关系数可以用来评估不同投资指标之间的相关程度，帮助投资者选择最有效的投资指标。
8. 衡量不同投资产品之间的相关性：斯皮尔曼相关系数可以用来衡量不同投资产品之间的相关程度，帮助投资者选择最合适的投资组合。
9. 评估不同地区之间的风险关联：斯皮尔曼相关系数可以用来评估不同地区之间的风险关联程度，帮助投资者进行国际投资和跨境资产配置。
10. 比较不同投资期限之间的风险：斯皮尔曼相关系数可以用来比较不同投资期限之间的风险水平，帮助投资者选择合适的投资期限。
多因子选股模型r语言代码
多因子选股模型r语言代码多因子选股模型是指利用多个因子（例如市盈率、市净率、ROE等）来筛选优质股票的一种投资策略。
在使用多因子选股模型时，需要先明确选股的目标和筛选标准，并根据这些标准选取合适的因子，然后使用R语言编写代码来筛选出符合条件的股票。
以下是一个简单的多因子选股模型的R语言代码：# 导入所需要的包library(quantmod)# 设定选股的筛选标准pe_threshold <- 20 # 市盈率阈值pb_threshold <- 3 # 市净率阈值roe_threshold <- 15 # ROE阈值# 获取股票代码列表stock_list <- c("AAPL", "GOOG", "IBM", "MSFT", "AMZN")# 循环遍历股票，检查是否符合筛选标准for (symbol in stock_list) {#获取股票的历史数据data <- getSymbols(symbol, from = "2000-01-01")# 计算市盈率、市净率和ROEpe_ratio <- tail(data$AAPL.Adjusted / data$AAPL.Earnings, 1) * 100pb_ratio <- tail(data$AAPL.Adjusted / data$AAPL.Book.Value, 1)roe <- tail(data$AAPL.Earnings / data$AAPL.Equity, 1) * 100# 判断是否符合条件if (pe_ratio <= pe_threshold && pb_ratio <= pb_threshold && roe >= roe_threshold) {# 符合条件的股票print(paste(symbol, "符合条件"))} else {# 不符合条件的股票print(paste(symbol, "不符合条件"))}}在上述代码中，我们设定了市盈率、市净率和ROE的阈值，然后使用循环遍历的方式，依次检查每个股票是否符合条件。
数据利他：全球治理观下基于公共利益的数据共享机制
数据科学数据利他：全球治理观下基于公共利益的数据共享机制*陈媛媛，赵晴*本文系国家社科基金重大项目“面向数字化发展的公共数据开放利用体系与能力建设研究”（项目编号:21&ZD337）、黑龙江省教育科学“十四五”规划2022年重点项目“高等院校大学生数据素养教育实践框架研究”（项目编号：GJB1422014）和黑龙江省省属高等学校基本科研业务费科研项目“数据要素市场参与者类别及动力机制研究”（项目编号：2022-KYYWF-1183）研究成果。
摘要鼓励出于利他目的的数据共享是发挥数据潜力、促进实现社会公共利益的重要方式，也是践行共商共建共享的全球治理观的重要路径。
文章围绕《数据治理法案》提出的“数据利他”概念进行研究，探讨数据利他的相关概念内涵、利益相关者及组织运行与实践，为我国数据开放与共享提供参考。
作为数据共享新机制，数据利他在发展之初会遇到一定阻碍，需要从获得利益相关者信任、激发利益相关者的参与动力、获取可持续的资金支持、解决组织运行中的技术难题、助力公共数据开放5个方面采取应对措施。
关键词数据利他公共利益数据治理法案数据共享引用本文格式陈媛媛，赵晴.数据利他：全球治理观下基于公共利益的数据共享机制[J].图书馆论坛，2023，43（5）：71-80.Data Altruism ：A Data Sharing Mechanism Based on Public Interest from the Perspective of Global GovernanceCHEN Yuanyuan &ZHAO QingAbstractEncouraging data sharing for altruistic purposes is an important way to reach the potential of data andpromote social and public interests ，as well as a critical path to pursue a vision of global governance featuring theprinciples of extensive consultation ，joint contribution and shared benefits.Focusing on the concept of "dataaltruism"proposed by the Data Governance Act ，this paper explores the meanings of related concepts ，the stakeholders and the organizational operation ，so as to provide reference for the practice of data opening and sharing in China.As a new mechanism for data sharing ，data altruism will inevitably encounter certain obstacles atthe beginning of development ，and it is necessary to respond in a proactive manner ，in terms of gaining the trust of stakeholders ，boosting the participation of stakeholders ，seeking for sustainable financing ，addressing technicalproblems in organizational operation ，and facilitating the open access to public data.Keywords data altruism ；public interest ；data governance act ；data sharing数据科学0引言大数据时代，数字技术改变社会经济运作方式，帮助人们获取参与和掌控数据的主动地位。
Applied Machine Learning
Applied Machine Learning Machine learning is a rapidly growing field that has the potential to revolutionize various industries and aspects of our daily lives. From healthcare to finance to transportation, the applications of machine learning are vast and diverse. However, with this potential comes a host of challenges and problems that need to be addressed in order for machine learning to reach its full potential. In this response, we will explore some of the key problems facing applied machine learning and discuss potential solutions and avenues for further research. One of the primary challenges in applied machine learning is the issue of bias in data and algorithms. Machine learning models are only as good as the data they are trained on, and if this data is biased or incomplete, the resulting model will also be biased. This can have serious implications in areas such as hiring, lending, and criminal justice, where biased algorithms can perpetuate and even exacerbate existing inequalities. Addressing this problem requires a multi-faceted approach, including careful curation of training data, transparency in algorithmic decision-making, and ongoing monitoring and evaluation of model performance. Another significant problem in applied machine learning is the issue of interpretability. Many machine learning models, particularly deep learning models, are often referred to as "black boxes" due to their complexity and lack of transparency. This can be a major barrier to their adoption in fields where interpretability is crucial, such as healthcare and finance. Researchers and practitioners are actively working on developing methods for making machine learning models more interpretable, such as through the use of attention mechanisms and model distillation techniques. However, this remains an ongoing area of research and development. In addition to bias and interpretability, another challenge in applied machine learning is the issue of scalability. While many machine learning algorithms have been shown to perform well on small-scale problems, scaling these algorithms to handle large and complex datasets remains a significant challenge. This is particularly true in fields such as genomics and climate science, where the volume and complexity of data are immense. Addressing this problem requires advances in both algorithms and computing infrastructure, as well as interdisciplinary collaboration between machine learning experts anddomain-specific researchers. Furthermore, the issue of ethical considerations in applied machine learning is becoming increasingly important. As machine learning technologies become more pervasive, the potential for misuse and unintended consequences also grows. This includes concerns around privacy, security, and the potential for autonomous systems to make decisions with significant societal impact. It is essential for researchers, practitioners, and policymakers to work together to develop ethical guidelines and frameworks for the responsible development and deployment of machine learning technologies. Another challenge in applied machine learning is the issue of data quality and quantity. In many real-world applications, obtaining labeled data for training machine learning models can be expensive and time-consuming. This is particularly true in domains such as healthcare and education, where obtaining ground truth labels may require significant expertise and resources. Advances in semi-supervised and unsupervised learning, as well as techniques for data augmentation and transfer learning, can help mitigate some of these challenges. However, there remains a need for further research into methods for learning from limited and noisy data. Finally, a key challenge in applied machine learning is the issue of reproducibility and robustness. Many machine learning research findings are difficult to reproduce, and models often fail to generalize to new, unseen data. This can be a significant barrier to the adoption of machine learning in critical applications where reliability is paramount. Addressing this problem requires a greater emphasis on rigorous experimental design, open science practices, and the development of benchmark datasets and evaluation metrics. In conclusion, applied machine learning holds tremendous promise for addressing some of the most pressing challenges facing society today. However, in order to realize this potential, it is essential to address a host of problems and challenges, including bias, interpretability, scalability, ethical considerations, data quality and quantity, and reproducibility and robustness. By working together across disciplines and sectors, researchers, practitioners, and policymakers can help ensure that machine learning technologies are developed and deployed in a responsible and impactful manner.。
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Food and beverage. The Linklaters Emerging Opportunity Index |21Case studyalways work in the emerging world where the economics can be challenging and you first need to establish your brand. In our industry, we find very few competitors who have significant presence in more than 10 markets. They will often have pockets of strength in certain markets but the key differentiating factor between success and failure in a country is the selection of the local partner.Critical success factorsWhen you start in an emerging market, it’s often the case that only 10% or 20% of consumers can afford your brand. So you need to create ways to enable people to access your products, and that can mean being unprofitable for a while. In our industry, it is critical that we prove the concept first, then prove the business model, and then multiply that model through rapid unit development. But the initial stages require significant up-front investment and a willingness to invest over a long period of time. As abenchmark, we think of it taking around ten years before we reach the third stage of rapid unit development. So when we go into a market, we are not immediately looking at profitability. Instead we arelooking at metrics like transactions, repeat business and customer preference for our Brand over local competitors that indicate whether our concept is working.We don’t always use our own equity. We have two ownership models that are100% franchise models and are typically applied in markets which are smaller or where there is a challenging environment. For instance, in many parts of Africa,we are currently going into countries that no other western QSR company is in, because we want that first-mover advantage. We are doing that through local franchise partners. We incentivise our franchise partners to take a long-term brand-building approach by asking them to commit to a 20-year contract. The Indian marketOur next challenge is India.Normally we would want to start our Brands in the very largest cities in a country. However, we found theeconomics of skyrocketing rental rates in Delhi and Mumbai very unattractive. We developed a cluster approach,labelling the next five largest cities, where rental rates were half those of Delhi or Mumbai, as ‘growth’ cities, and that is where we focused our efforts. Our next cluster of cities were the ‘emerging’ cities, where we would plant a flag, and then ‘future’ cities which were not yet ready for our Brand. And over time, the ‘future’ cities will become ‘emerging’ and the ‘emerging’ become ‘growth’, and our market presence will develop.Overall, we believe emerging economies can still deliver plenty of growth. In most emerging markets today, Yum! Brands has only one or two stores per million people. Our global benchmark is 20 stores per million people per Brand which is our level of penetration in the US. In China this means expanding from 4,000 stores today to between 10,000 and 20,000 stores as China develops.Yum! Brands is the biggest QSR (quick-service restaurant) player in the emerging markets. Its international division has recently been growing at 10% per year, and the company believes it still has ample room to grow in the emerging world.Neil Thomson, VP Finance, Yum! RestaurantsInternational, discusses what makes his company’s emerging-market positioning so successful.‘Equity leadership’What is unusual about our emerging markets strategy is that Yum! is prepared to invest its own capital. This is an important ingredient in our success. We call it ‘equity leadership’. Our China business was built with 100% equityownership and it is a pure organic growth story. However, even in markets where we have local franchisees, for instance in India, our equity investment was the catalyst for exponential growth in our KFC Brand. It’s a similar story for KFC in Russia, where we have invested equity and seen tremendous growth in unit sales volumes. There are a number of advantages for us in having our own capital invested. For example, we can ensure that we make the right decisions for the Brand, such as selecting the very best sites, over hiring for great local talent or setting prices at a very attractive level for consumers.Relying 100% on franchisees to lead growth means it is critical that you have the right partner. In our experience, a local partner will often be looking to make a return very quickly, and that doesn’tYum! Brands in emerging marketsNeil Thomson VP Finance,Yum! RestaurantsInternational。