Using Power Diagrams to Build Optimal Unstructured Meshes for C-Grid Models

The Model for Prediction Across Scales (MPAS) for Ocean (-O), Sea-Ice (-SI) and Land-Ice (-LI), in addition to the Coastal Ocean Marine Prediction Across Scales (COMPAS) are two novel general circulation models designed to resolve coupled ocean-ice dynamics over variable spatial scales using non-uniform unstructured grids. Both models are based on a conservative mimetic finite-difference/volume formulation (TRiSK), in which staggered momentum, vorticity and mass-based degrees- of-freedom are distributed over an orthogonal 'primal-dual' mesh

Examining Variability in Superintendent Community Involvement

This study examined the extent to which four independent variables (age, gender, education level, and district type) accounted for variability in superintendent community involvement. Two covariates associated with levels of community involvement (disposition toward community involvement and district enrollment) were infused to assess the impact of the independent variables. Analysis revealed that the model accounted for 8% of the variance as indicated both by R2 and by adjusted R2. Given the number of respondents (1,867), this is considered a medium effect having practical implications in the applied setting. Among the four independent variables, only a single main effect (district type) was found

Job Satisfaction of Female and Male Superintendents: The Influence of Job Facets and Contextual Variables as Potential Predictors

A descriptive multiple regression approach was used to assess the job satisfaction of female and male public school superintendents taking part in a decennial survey conducted by AASA. Self-reported job satisfaction of public school superintendents was regressed on their affective reactions to specific job facets (supervision, co-workers, and compensation) and to contextual variables (type of school district, legislative mandates, and funding sources) purported to influence their job satisfaction. Results indicate that female and male superintendents were found to be similarly satisfied with their current job assignment but for different reasons as revealed by interaction terms addressed in the regression analyses

Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values

In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information Processing Systems 2019 (NeurIPS). We examine how our combination of established ideas achieves competitive performance on semi-realistic and realistic time series data exhibiting common challenges in real-world Earth sciences data. In particular, we discuss a) a rationale for leveraging linear methods to identify causal links in non-linear systems, b) a simulation-backed explanation as to why large regression coefficients may predict causal links better in practice than small p-values and thus why normalising the data may sometimes hinder causal structure learning. For benchmark usage, we detail the algorithms here and provide implementations at https://github.com/sweichwald/tidybench . We propose the presented competition-proven methods for baseline benchmark comparisons to guide the development of novel algorithms for structure learning from time series