Physical Exercise and the Prosocial Behaviors of Students in China

Based on the 2014-2015 China Education Panel Survey (CEPS) and using the propensity score matching method, we studied the causal relationship between physical exercise and prosocial behavior of junior middle school students in China. Ordinary least squares regression and propensity score matching estimation results showed that participation in physical exercise significantly increases students’ prosocial behavior by more than 0.2 standard points. The results of this study were tested and found to be robust.

Analysing Higher Educational Institutions' Role in Fulfilling their Third Mission

The transitional role of higher educational institutions encourages attention to initiatives aimed at positioning these institutions as hubs of knowledge production and transfers capable of influencing regional development. Nonetheless, the literature has defied the extent to which these institutions have well-embraced their third mission of impacting regional development, thus calling for further approaches in examining the role of these institutions. This paper evaluates the various ways higher educational institutions have embraced their third mission of contributing to regional development. We sourced data containing information from 164 Higher Education Institutions (HEIs) located across the United Kingdom employing the propensity score matching estimation model. The results of the Average Treatment Effects (ATE) highlight the additionality effects of HEIs on graduate support, attracting inward investment, R&D collaborations and network facilitation. Surprisingly, our results emphasis the lack of significance in HEIs role in supporting SMEs, and knowledge exchanges. The main implication relates to the challenges in adopting initiatives that proved successful in specific universities to other higher educational settings.

Score matching filters for Gaussian Markov random fields with a linear model of the precision matrix

<p style='text-indent:20px;'>We present an ensemble filtering method based on a linear model for the precision matrix (the inverse of the covariance) with the parameters determined by Score Matching Estimation. The method provides a rigorous covariance regularization when the underlying random field is Gaussian Markov. The parameters are found by solving a system of linear equations. The analysis step uses the inverse formulation of the Kalman update. Several filter versions, differing in the construction of the analysis ensemble, are proposed, as well as a Score matching version of the Extended Kalman Filter.</p>

Shape Similarity Measurement for Known-Object Localization: A New Normalized Assessment

This paper presents a new, normalized measure for assessing a contour-based object pose. Regarding binary images, the algorithm enables supervised assessment of known-object recognition and localization. A performance measure is computed to quantify differences between a reference edge map and a candidate image. Normalization is appropriate for interpreting the result of the pose assessment. Furthermore, the new measure is well motivated by highlighting the limitations of existing metrics to the main shape variations (translation, rotation, and scaling), by showing how the proposed measure is more robust to them. Indeed, this measure can determine to what extent an object shape differs from a desired position. In comparison with 6 other approaches, experiments performed on real images at different sizes/scales demonstrate the suitability of the new method for object-pose or shape-matching estimation.