Heun's equation, generalized hypergeometric function and exceptional Jacobi polynomial

We study Heun's differential equation in the case that one of the singularities is apparent. In particular we conjecture a relationship with generalized hypergeometric differential equation and establish it in some cases. We apply our results to exceptional Jacobi polynomials.Comment: 15 pages; validity of the conjecture was extende

Out-of-plane dielectric constant and insulator-superconductor transition in Bi_2Sr_2Dy_{1-x}Er_xCu_2O_8 single crystals

The out-of-plane dielectric constant of the parent insulator of the high-temperature superconductor Bi_2Sr_2(Dy,Er)Cu_2O_8 was measured and analysed from 80 to 300 K in the frequency range of 10^6-10^9 Hz. All the samples were found to show a fairly large value of 10-60, implying some kind of charge inhomogeneity in the CuO_2 plane. Considering that the superconducting sample Bi_2Sr_2(Ca,Pr)Cu_2O_8 also shows a similar dielectric constant, the charge inhomogeneity plays an important role in the insulator-superconductor transition.Comment: RevTex4 format, 5 pages, 3 figures, submitted to J. Phys. Condens. Ma

Detecting Drowsy Learners at the Wheel of e-Learning Platforms with Multimodal Learning Analytics

Learners are expected to stay wakeful and focused while interacting with e-learning platforms. Although wakefulness of learners strongly relates to educational outcomes, detecting drowsy learning behaviors only from log data is not an easy task. In this study, we describe the results of our research to model learners’ wakefulness based on multimodal data generated from heart rate, seat pressure, and face recognition. We collected multimodal data from learners in a blended course of informatics and conducted two types of analysis on them. First, we clustered features based on learners’ wakefulness labels as generated by human raters and ran a statistical analysis. This analysis helped us generate insights from multimodal data that can be used to inform learner and teacher feedback in multimodal learning analytics. Second, we trained machine learning models with multiclass-Support Vector Machine (SVM), Random Forest (RF) and CatBoost Classifier (CatBoost) algorithms to recognize learners’ wakefulness states automatically. We achieved an average macro-F1 score of 0.82 in automated user-dependent models with CatBoost. We also showed that compared to unimodal data from each sensor, the multimodal sensor data can improve the accuracy of models predicting the wakefulness states of learners while they are interacting with e-learning platforms