Fusing Binary Interface Defects in Topological Phases: The Vec⁡(Z/pZ)\operatorname{Vec}(\mathbb{Z}/p\mathbb{Z}) case

A binary interface defect is any interface between two (not necessarily invertible) domain walls. We compute all possible binary interface defects in Kitaev's $\mathbb{Z}/p\mathbb{Z}$ model and all possible fusions between them. Our methods can be applied to any Levin-Wen model. We also give physical interpretations for each of the defects in the $\mathbb{Z}/p\mathbb{Z}$ model. These physical interpretations provide a new graphical calculus which can be used to compute defect fusion.Comment: 27+10 pages, 2+5 tables, comments welcom

Examining the Predictability of the Teacher Reported Student Risk Screening Scale- Internalizing and Externalizing (SRSS-IE) Tool Versus the Student Reported Strengths and Difficulties Questionnaire (SDQ) Tool

Introduction: The purpose of this study was to examine results from one school that conducted universal mental health screening using both teacher-report via the Student Risk Screening Scale, Internalizing/Externalizing (SRSS-IE) and student report via the Strengths and Difficulties Questionnaire (SDQ). Through this study we compared different informants and different screening tools that are available to screen for mental health risk. Aim: To determine if there is a relationship between teacher ratings on the SRSS-IE and student ratings on the SDQ, and to assess the predictability of each respondent group for office discipline referrals and absences. Method: Non-parametric correlation tests were conducted between the SRSS-IE and SDQ externalizing and internalizing scores. Generalized linear regression models were created based on the data (demographics, internalizing and externalizing scores on both screening tools) to model the two count outcomes (ODRs and absences). ROC curves were generated to calculate the diagnostic accuracy metrics of the scores on both the SRSS-IE and SDQ with the outcome measures (ODRs and absences). Results: Externalizing scores on both screening tools had statistically significant low correlations. The ODR model contained two predictors: externalizing score on the SRSS-IE and internalizing score on the SDQ, while the absence model contained gender, grade, race, and the externalizing score on the SDQ. The highest accuracy and agreement values were seen between students with elevated risks on both tools and ODRs. Discussion: The results confirmed that teacher and student reporting, as well as different screening tools, will result in some different students being identified. The choice of informant and screening tool should be dependent on the needs and resources of the school