基于PSM-DID的教育对性别收入差距的影响研究

文章基于CGSS2015-2021年的数据,运用倾向得分匹配法和最小二乘OLS回归,构建反事实框架,探究了中等教育和高等教育对于性别收入差距的影响是否显著。结果表明,随着我国教育程度的逐渐普及,中等教育可以使得性别收入差距缩小16.0%,高等教育可以使得性别收入差距缩小9.6%,同时,接受相应教育程度的群体的平均收入水平要高于没有接受相应教育程度的群体的平均收入水平。由此得出结论,中等教育和高等教育通过提升女性平均人力资本水平,可以有效地缩小具有相同接受教育机会的女性和男性收入水平的差异。

基于PSM的中国新能源汽车企业绩效分析

基于2001—2020年中国汽车业A股上市公司财务数据,本文首先采用线性回归和倾向得分匹配(PSM)方法分阶段对新能源汽车企业财务绩效展开实证分析。通过检验企业是否具有盈利和研发优势,对我国新能源汽车补贴实施效果进行验证。实证结果表明,补贴能提高新能源汽车企业盈利能力,但不能促进企业研发创新。后补贴时代,我国新能源汽车发展应由以政府主导为主转向以市场激励为主,积极推进双积分办法与碳交易市场的衔接,推进我国新能源汽车产业链实现碳中和。

政府数据开放平台建设对促进数字政府发展效果的影响——基于PSM模型的计量分析

[目的/意义]政府数据开放是政府数字化发展的方向,探究政府数据开放平台(OGDP)对数字政府发展的影响,对推进实现国家治理体系和治理能力现代化目标具有指导意义。[方法/过程]文章基于OGDP的建设与否,运用倾向得分匹配(PSM)方法,以全国101个地级市2019年的截面数据为研究样本进行实证分析,探究OGDP建设对数字政府发展的促进作用。[结果/结论]在克服样本选择偏误以及尽可能消除不可观测因素带来的内生性影响的情况下,研究发现OGDP的建设能够正向影响数字政府发展,建设OGDP的城市的数字政府发展效应要比未建设的高15%—25%。因此,建议从加强OGDP建设、提升数据开放主动性和质量、优化平台服务、建设国家级OGDP等方面促进数字政府发展。

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

职业教育真的会加剧社会阶层固化吗?——基于倾向得分匹配(PSM)的分析

职业教育在促进我国社会结构合理流动中扮演着至关重要的角色。基于中国劳动力动态调查(CLDS)数据,采用倾向得分匹配法(PSM),检验职业教育是否会加剧我国社会阶层固化。研究发现,职业教育与非职业教育群体在户籍、父母职业类型、父母受教育程度、自身职业类型、经济收入等方面存在显著性差异。消减“自选择性”偏差后,中等教育层次中,中职教育能促进代内阶层向上流动;高等教育层次中,高职教育会加剧代际阶层固化。其背后深层机制在于,式微的中职教育在社会排挤效应下最终仅能指向有限的个人发展;膨胀的文凭效应不断提高主要劳动力市场的进入门槛,挤压了高职教育群体进入高地位职业类型的通道。为此,建议从提升职业教育学历层次,提高中职教育升学率,推行“职业教育高端人才专项培养计划”试点等方面入手提高职业教育吸引力。

A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

高等教育促进社会阶层流动了吗?——基于CGSS数据的PSM实证分析

基于2013年、2015年、2017年和2018年“中国综合社会调查”混合截面数据,采用准自然实验PSM方法以克服自选择性和内生性问题,探讨高等教育促进社会阶层流动的工具性作用。结果表明:(1)高等教育仍是促进社会阶层流动的渠道之一,但克服自选择性和内生性后,高等教育促进社会阶层流动的“净效应”大大降低;(2)高等教育促进社会阶层流动存在感知社会阶层流动、预期社会阶层流动和代际社会阶层流动的异质性,高等教育不能使感知社会阶层得到显著提升,但却能显著促进预期和代际社会阶层向上流动;(3)扩招后高等教育不能显著促进社会阶层向上流动,高等教育不能显著促进农业户口、弱势经济地位家庭子女社会阶层流动向上流动,高等教育“阶层复制”的工具性作用有所强化。

按病种付费对中医优势病种住院费用的影响研究——基于PSM-DID法

目的:分析按病种付费对中医优势病种住院费用的影响,为中医按病种付费改革提供参考。方法:采用某中医医院实施按病种付费的高位肛瘘患者住院费用数据,经倾向得分匹配法(PSM)匹配后,进行双重差分(DID)回归分析,探讨按病种付费对中医优势病种住院费用的影响。结果:实施按病种付费后,高位肛瘘患者住院总费用下降了1067.63元、自付费用下降了656.66元、住院天数缩短了5.54天、西药费增长了406.55元、中成药费下降了126.80元。结论:在中医优势病种按病种付费过程中,应加强医保监督,持续完善、更新中医信息系统,扩大按病种付费的中医优势病种范围,突出中医临床路径优势,促进中医药传承创新发展。

Feature Matching via Topology-Aware Graph Interaction Model

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.

Multiple Matching Attenuation Based on Curvelet Domain Extended Filtering

The paper develops a multiple matching attenuation method based on extended filtering in the curvelet domain,which combines the traditional Wiener filtering method with the matching attenuation method in curvelet domain.Firstly,the method uses the predicted multiple data to generate the Hilbert transform records,time derivative records and time derivative records of Hilbert transform.Then,the above records are transformed into the curvelet domain and multiple matching attenuation based on least squares extended filtering is performed.Finally,the attenuation results are transformed back into the time-space domain.Tests on the model data and field data show that the method proposed in the paper effectively suppress the multiples while preserving the primaries well.Furthermore,it has higher accuracy in eliminating multiple reflections,which is more suitable for the multiple attenuation tasks in the areas with complex structures compared to the time-space domain extended filtering method and the conventional curvelet transform method.

A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

Distributed Matching Theory-Based Task Re-Allocating for Heterogeneous Multi-UAV Edge Computing

Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle(UAV)edge computing.However,the heterogeneity of UAV computation resource,and the task re-allocating between UAVs have not been fully considered yet.Moreover,most existing works neglect the fact that a task can only be executed on the UAV equipped with its desired service function(SF).In this backdrop,this paper formulates the task scheduling problem as a multi-objective task scheduling problem,which aims at maximizing the task execution success ratio while minimizing the average weighted sum of all tasks’completion time and energy consumption.Optimizing three coupled goals in a realtime manner with the dynamic arrival of tasks hinders us from adopting existing methods,like machine learning-based solutions that require a long training time and tremendous pre-knowledge about the task arrival process,or heuristic-based ones that usually incur a long decision-making time.To tackle this problem in a distributed manner,we establish a matching theory framework,in which three conflicting goals are treated as the preferences of tasks,SFs and UAVs.Then,a Distributed Matching Theory-based Re-allocating(DiMaToRe)algorithm is put forward.We formally proved that a stable matching can be achieved by our proposal.Extensive simulation results show that Di Ma To Re algorithm outperforms benchmark algorithms under diverse parameter settings and has good robustness.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

CMMCAN:Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention

In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities.However,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and extraction.This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for patients.To address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of EfficientViT.Additionally,a novel lightweight feature extraction and matching network based on attention mechanism is proposed.This network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese network.It extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur scenarios.Global and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level features.Extensive ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic datasets.Experimental results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage efficiency.When compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense models.Furthermore,this method increases the F1 score by 33.2%and accelerates runtime by 70.2%.It is noteworthy that the speed of CMMCAN surpasses that of comparative lightweight models,with feature extraction and matching performance comparable to existing complex models but with faster speed and higher cost-effectiveness.

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

基于PSM-DID分析创建国家卫生城市对我国231个地级市环境水平的影响

目的分析创建国家卫生城市(简称“创卫”)对我国地级市环境水平的影响。方法本研究纳入我国2007-2019年期间的104个卫生城市,通过将倾向性得分匹配(propensity score matching,PSM)与双重差分法(difference-in-difference,DID)相结合的方法,选择127个城市作为对照组,分析“创卫”活动对我国地级市环境水平的影响。反映环境水平的指标包括生活垃圾无害化处理率、污水处理厂处理率、废水排放量、烟粉尘排放量和绿化覆盖率,调整经济发展水平、人口密度、技术创新程度、工业化程度、年均温度、年均相对湿度、年均日照时间、年均风速和年均气压,通过假设政策分别提前3、4和5年和随机化处理组和对照组,进行安慰剂检验。结果创建卫生城市使垃圾处理率、污水处理率、市辖区绿化覆盖率分别显著提高11.8%(P=0.038)、8.1%(P=0.035)和16.5%(P=0.016),废水排放显著下降9.5%(P=0.049)。结论“创卫”之后,地级市的垃圾处理率、污水处理率和市辖区绿化覆盖率水平显著提高。

基于PSM-DID模型的康复病例DRG付费实证研究

目的探讨按疾病诊断相关分组(DRG)付费对康复病例的影响,针对康复病组进行DRG付费效果评价并提出相关建议。方法抽取2020—2021年四川省定点医疗机构收治的康复病例,经倾向性得分匹配后利用双重差分法进行回归分析,评估DRG付费对康复病例住院费用、住院日的影响,分析政策净效应。结果DRG付费实施后,康复病例住院总费用、药品费、治疗费、检查费都存在不同程度的下降,只有床位费有所上升。其中,住院总费用下降了21.8%(P<0.05),病例费用结构较DRG付费前也发生一定变化。此外,康复病例住院日并未在DRG政策导向下有效缩短,反而增加了13.4%(P<0.05)。结论DRG付费能够有效减轻康复病种患者负担,但并未优化医院在康复病种方面的服务效率,DRG付费是否适用于康复病种有待进一步商榷,医保部门与医院应当紧密联系,积极探索康复病例的医保支付新模式。

Matching Dyeing and Properties of Silk Fabrics with Natural Edible Pigments

The silk fabrics were matching dyed with three natural edible pigments(red rice red,ginger yellow and gardenia blue).By investigating the dyeing rates and lifting properties of these pigments,it was observed that their compatibilities were excellent in the dyeing process:dye dosage 2.5%(omf),mordant alum dosage 2.0%(omf),dyeing temperature 80℃and dyeing time 40 min.The silk fabrics dyed with secondary colors exhibited vibrant and vivid color owing to the remarkable lightness and chroma of ginger yellow.However,gardenia blue exhibited multiple absorption peaks in the visible light range,resulting in significantly lower lightness and chroma for the silk fabrics dyed with tertiary colors,thus making it suitable only for matte-colored fabrics with low chroma levels.In addition,the silk fabrics dyed with these three pigments had a color fastness that exceeded grade 3 in resistance to perspiration,soap washing and light exposure,indicating acceptable wearing properties.The dyeing process described in this research exhibited a wide range of potential applications in matching dyeing of protein-based textiles with natural colorants.

Inversion of Seabed Geotechnical Properties in the Arctic Chukchi Deep Sea Basin Based on Time Domain Adaptive Search Matching Algorithm

The chirp sub-bottom profiler,for its high resolution,easy accessibility and cost-effectiveness,has been widely used in acoustic detection.In this paper,the acoustic impedance and grain size compositions were obtained based on the chirp sub-bottom profiler data collected in the Chukchi Plateau area during the 11th Arctic Expedition of China.The time-domain adaptive search matching algorithm was used and validated on our established theoretical model.The misfit between the inversion result and the theoretical model is less than 0.067%.The grain size was calculated according to the empirical relationship between the acoustic impedance and the grain size of the sediment.The average acoustic impedance of sub-seafloor strata is 2.5026×10^(6) kg(s m^(2))^(-1)and the average grain size(θvalue)of the seafloor surface sediment is 7.1498,indicating the predominant occurrence of very fine silt sediment in the study area.Comparison of the inversion results and the laboratory measurements of nearby borehole samples shows that they are in general agreement.

Automatic depth matching method of well log based on deep reinforcement learning

In the traditional well log depth matching tasks,manual adjustments are required,which means significantly labor-intensive for multiple wells,leading to low work efficiency.This paper introduces a multi-agent deep reinforcement learning(MARL)method to automate the depth matching of multi-well logs.This method defines multiple top-down dual sliding windows based on the convolutional neural network(CNN)to extract and capture similar feature sequences on well logs,and it establishes an interaction mechanism between agents and the environment to control the depth matching process.Specifically,the agent selects an action to translate or scale the feature sequence based on the double deep Q-network(DDQN).Through the feedback of the reward signal,it evaluates the effectiveness of each action,aiming to obtain the optimal strategy and improve the accuracy of the matching task.Our experiments show that MARL can automatically perform depth matches for well-logs in multiple wells,and reduce manual intervention.In the application to the oil field,a comparative analysis of dynamic time warping(DTW),deep Q-learning network(DQN),and DDQN methods revealed that the DDQN algorithm,with its dual-network evaluation mechanism,significantly improves performance by identifying and aligning more details in the well log feature sequences,thus achieving higher depth matching accuracy.