The Gini Coefficient’s Magic Does Not Work on Standardized Test Scores

The Gini coefficient, an indicator that is often used to measure the inequality in the distribution of income within countries, is meaningless when used on standardized test scores. This is because the value of the Gini coefficient depends on the scale’s mean and standard deviation which are arbitrarily selected by the test developers. Keeping the standard deviation of the scale constant, increasing the mean will decrease the Gini coefficient, while keeping the mean of the scale constant, increasing the standard deviation will increase the Gini coefficient. In addition, when Gini coefficients are estimated with scores on two different scales, not only the values of the Gini coefficients but also the country rankings of the Gini coefficients will change. Therefore, for standardized test scores, the value of the Gini coefficient is meaningless, as is comparing the size of the Gini coefficients estimated from different countries. More generally, all relative measures of dispersion, including the Gini coefficient, are meaningless for interval scales (i.e., a scale in which the distance between any two consecutive points are equal, but the scale does not have an absolute zero), such as standardized test scores

Essential practices for early childhood educators who value multicultural perspectives

textThis report addresses the importance of multicultural education in early childhood classrooms as well as three essential practices for early childhood educators who value multicultural perspectives. The early childhood classroom is the first place in which children develop their identities and recognize cultural differences. Multicultural education can offer opportunities for children to value and understand cultural diversity as they have more experiences outside of their homes and neighborhoods. While there are many kinds of practices that support a multicultural perspective, this paper focuses on three multicultural practices that early childhood educators can incorporate in their classrooms in order to create authentic multicultural classrooms and to promote multiculturalism. The three practices are 1) integrating culturally relevant pedagogy/culturally responsive teaching, 2) understanding multicultural families, and 3) pursuing social justice. These practices can help early childhood educators better understand multicultural students and families and have more meaningful interactions and partnership opportunities with them.Curriculum and Instructio

Data Center Based on Cloud Computing Technology

With the rapid development of Internet applications, the impact on the development of data centers is huge. Domestic data centers attach great importance to the acceptance of cloud computing technology and the construction of application systems. Nowadays, data centers can be effectively transformed into cloud computing development. The operating support environment has become the main consideration and focus of today’s data center development. Under the concept of cloud computing, this article analyzes and builds a new data center that is more in line with the needs of resource management and information construction. Taking the development of data centers based on cloud computing technology as the research object, building data centers through cloud computing technology realizes the acquisition and organization of data and makes full use of resources. A new information resource management system with functions such as classification and query of data, overall processing and analysis of data, backup of data, information management and services has been realized. Before using the cloud computing model, the network deployed a total of 40 virtual servers, and the average CPU utilization rate was less than 40%. Since the establishment of the data center model in this article, the utilization rate of the processor has stabilized at around 95%. Therefore, the data center proposed in this paper greatly improves the utilization of data and speeds up the overall construction of the data center

Topical Application of Chrysanthemum indicum L. Attenuates the Development of Atopic Dermatitis-Like Skin Lesions by Suppressing Serum IgE Levels, IFN-γ, and IL-4 in Nc/Nga Mice

Chrysanthemum indicum L. (CIL) is widely used as an anti-inflammatory agent in Asia and our preliminary study revealed that CIL reduced interleukin (IL)-4 and IL-13 in 2,4-dinitrochlorobenzene (DNCB)-treated HaCaT cells, a human keratinocyte cell line. We investigated the atopic dermatitis (AD) effect of topically applied CIL in mice with AD-like symptoms. After topical application of 1,3-butylen glycol (control), CIL-Low (5%), CIL-High (30%), or 0.1% hydrocortisone (HC) on the AD-like skin lesions in DNCB-treated NC/Nga mice for 5 weeks, the ear thickness, mast cell infiltration, and serum immunoglobulin E (IgE), IgG1, IL-4 and interferon (IFN)-γ were measured. The gene expressions of IL-4, IL-13, and IFN-γ in the dorsal skin were assayed. CIL treatment dosedependently reduced severity of clinical symptoms of dorsal skin, ear thickness, and the number of mast cells and eosinophils. CIL-High significantly decreased serum IgE, IgG1, IL-4, and IFN-γ levels and reduced mRNA levels of IFN-γ, IL-4, and IL-13 in dorsal skin lesion. The improvement by CIL-High was similar to HC, but without its adverse effects such as skin atrophy maceration, and secondary infection. In conclusion, CIL may be an effective alternative substance for the management of AD

Bayesian Approach to Linear Bayesian Networks

This study proposes the first Bayesian approach for learning high-dimensional linear Bayesian networks. The proposed approach iteratively estimates each element of the topological ordering from backward and its parent using the inverse of a partial covariance matrix. The proposed method successfully recovers the underlying structure when Bayesian regularization for the inverse covariance matrix with unequal shrinkage is applied. Specifically, it shows that the number of samples $n = \Omega( d_M^2 \log p)$ and $n = \Omega(d_M^2 p^{2/m})$ are sufficient for the proposed algorithm to learn linear Bayesian networks with sub-Gaussian and 4m-th bounded-moment error distributions, respectively, where $p$ is the number of nodes and $d_M$ is the maximum degree of the moralized graph. The theoretical findings are supported by extensive simulation studies including real data analysis. Furthermore the proposed method is demonstrated to outperform state-of-the-art frequentist approaches, such as the BHLSM, LISTEN, and TD algorithms in synthetic data

QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors

Replicating a user's pose from only wearable sensors is important for many AR/VR applications. Most existing methods for motion tracking avoid environment interaction apart from foot-floor contact due to their complex dynamics and hard constraints. However, in daily life people regularly interact with their environment, e.g. by sitting on a couch or leaning on a desk. Using Reinforcement Learning, we show that headset and controller pose, if combined with physics simulation and environment observations can generate realistic full-body poses even in highly constrained environments. The physics simulation automatically enforces the various constraints necessary for realistic poses, instead of manually specifying them as in many kinematic approaches. These hard constraints allow us to achieve high-quality interaction motions without typical artifacts such as penetration or contact sliding. We discuss three features, the environment representation, the contact reward and scene randomization, crucial to the performance of the method. We demonstrate the generality of the approach through various examples, such as sitting on chairs, a couch and boxes, stepping over boxes, rocking a chair and turning an office chair. We believe these are some of the highest-quality results achieved for motion tracking from sparse sensor with scene interaction