Child Psychology in the Dance Classroom

Child psychology is a discipline that studies the laws of children’s mental activities, and with the improvement of social standards, the application of psychology in children’s education is becoming more and more widespread. This paper takes children’s psychological development as a fulcrum to gain deep insight into children’s psychological characteristics in dance teaching. Taking children as the research object, it studies the specific application of psychology in the process of children’s dance teaching through its own teaching practice

Robust Estimation of High-Dimensional Mean Regression

Data subject to heavy-tailed errors are commonly encountered in various scientific fields, especially in the modern era with explosion of massive data. To address this problem, procedures based on quantile regression and Least Absolute Deviation (LAD) regression have been devel- oped in recent years. These methods essentially estimate the conditional median (or quantile) function. They can be very different from the conditional mean functions when distributions are asymmetric and heteroscedastic. How can we efficiently estimate the mean regression functions in ultra-high dimensional setting with existence of only the second moment? To solve this problem, we propose a penalized Huber loss with diverging parameter to reduce biases created by the traditional Huber loss. Such a penalized robust approximate quadratic (RA-quadratic) loss will be called RA-Lasso. In the ultra-high dimensional setting, where the dimensionality can grow exponentially with the sample size, our results reveal that the RA-lasso estimator produces a consistent estimator at the same rate as the optimal rate under the light-tail situation. We further study the computational convergence of RA-Lasso and show that the composite gradient descent algorithm indeed produces a solution that admits the same optimal rate after sufficient iterations. As a byproduct, we also establish the concentration inequality for estimat- ing population mean when there exists only the second moment. We compare RA-Lasso with other regularized robust estimators based on quantile regression and LAD regression. Extensive simulation studies demonstrate the satisfactory finite-sample performance of RA-Lasso

Economic valuation of development projects : a case study of a non-motorized transport project in India

One of the major difficulties in doing cost-benefit analysis of a development project is to estimate the total economic value of project benefits, which are usually multi-dimensional andinclude goods and services that are not traded in the market. Challenges also arise in aggregating the values of different benefits, which may not be mutually exclusive. This paper uses a contingent valuation approach to estimate the economic value of a non-motorized transport project in Pune, India, across beneficiaries. The heads of households which are potentially affected by the project are presented with a detailed description of the project, and then are asked to vote on whether such a project should be undertaken given different specifications of costs to the households. The total value of the project is then derived from the survey answers. Econometric analysis indicates that the survey responses provide generally reasonable valuation estimates.Transport Economics Policy&Planning,Environmental Economics&Policies,Roads&Highways,Housing&Human Habitats,Economic Theory&Research

Valuing water quality improvement in China : a case study of lake Puzhehei in Yunnan province

While polluted surface water is encountered across most of China, few economic valuation studies have been conducted on water quality changes. Limited information about the economic values associated with those potential water quality improvements or deteriorations is a disadvantage for making proper choices in water pollution control and clean-up activities. This paper reports an economic valuation study conducted in Yunnan, China, which aims to estimate the total value of a real investment project to improve the water quality of Lake Puzhehei by one grade level. Located in Qiubei County, which is far from large cities, the lake has been experiencing fast water quality deterioration in the past years. A conservative estimation strategy shows that on average a household located in Qiubei County is willing to pay about 30 yuan per month continuously for 5 years for water quality improvement, equivalent roughly to 3 percent of household income. The elasticity of willingness-to-pay with respect to income is estimated to be 0.21. The economic rate of return of the proposed project is estimated to be 18 percent, indicating a strong demand and high efficiency of investment in water quality improvement in China. This study also demonstrates that previous knowledge about water quality changes and the project may have a significant positive impact on people's valuation, and that the interviewer effect on valuation can be negative.Water and Industry,Environmental Economics&Policies,Water Supply and Sanitation Governance and Institutions,Town Water Supply and Sanitation,Water Supply and Systems

Simulation of pulsatile flow in baffled permeable channel for membrane filtration system

SIGLEAvailable from British Library Document Supply Centre- DSC:DX175185 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Competitive Ensembling Teacher-Student Framework for Semi-Supervised Left Atrium MRI Segmentation

Semi-supervised learning has greatly advanced medical image segmentation since it effectively alleviates the need of acquiring abundant annotations from experts and utilizes unlabeled data which is much easier to acquire. Among existing perturbed consistency learning methods, mean-teacher model serves as a standard baseline for semi-supervised medical image segmentation. In this paper, we present a simple yet efficient competitive ensembling teacher student framework for semi-supervised for left atrium segmentation from 3D MR images, in which two student models with different task-level disturbances are introduced to learn mutually, while a competitive ensembling strategy is performed to ensemble more reliable information to teacher model. Different from the one-way transfer between teacher and student models, our framework facilitates the collaborative learning procedure of different student models with the guidance of teacher model and motivates different training networks for a competitive learning and ensembling procedure to achieve better performance. We evaluate our proposed method on the public Left Atrium (LA) dataset and it obtains impressive performance gains by exploiting the unlabeled data effectively and outperforms several existing semi-supervised methods.Comment: Accepeted for BIBM 202

Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions

Data subject to heavy-tailed errors are commonly encountered in various scientific fields. To address this problem, procedures based on quantile regression and Least Absolute Deviation (LAD) regression have been developed in recent years. These methods essentially estimate the conditional median (or quantile) function. They can be very different from the conditional mean functions, especially when distributions are asymmetric and heteroscedastic. How can we efficiently estimate the mean regression functions in ultra-high dimensional setting with existence of only the second moment? To solve this problem, we propose a penalized Huber loss with diverging parameter to reduce biases created by the traditional Huber loss. Such a penalized robust approximate quadratic (RA-quadratic) loss will be called RA-Lasso. In the ultra-high dimensional setting, where the dimensionality can grow exponentially with the sample size, our results reveal that the RA-lasso estimator produces a consistent estimator at the same rate as the optimal rate under the light-tail situation. We further study the computational convergence of RA-Lasso and show that the composite gradient descent algorithm indeed produces a solution that admits the same optimal rate after sufficient iterations. As a byproduct, we also establish the concentration inequality for estimating population mean when there exists only the second moment. We compare RA-Lasso with other regularized robust estimators based on quantile regression and LAD regression. Extensive simulation studies demonstrate the satisfactory finite-sample performance of RA-Lasso

A Security Framework for Wireless Sensor Networks Utilizing a Unique Session Key

Key management is a core mechanism to ensure the security of applications and network services in wireless sensor networks. It includes two aspects: key distribution and key revocation. Many key management protocols have been specifically designed for wireless sensor networks. However, most of the key management protocols focus on the establishment of the required keys or the removal of the compromised keys. The design of these key management protocols does not consider the support of higher level security applications. When the applications are integrated later in sensor networks, new mechanisms must be designed. In this paper, we propose a security framework, uKeying, for wireless sensor networks. This framework can be easily extended to support many security applications. It includes three components: a security mechanism to provide secrecy for communications in sensor networks, an efficient session key distribution scheme, and a centralized key revocation scheme. The proposed framework does not depend on a specific key distribution scheme and can be used to support many security applications, such as secure group communications. Our analysis shows that the framework is secure, efficient, and extensible. The simulation and results also reveal for the first time that a centralized key revocation scheme can also attain a high efficiency