Trumpeting M Dwarfs with CONCH-SHELL: a Catalog of Nearby Cool Host-Stars for Habitable ExopLanets and Life

We present an all-sky catalog of 2970 nearby ($d \lesssim 50$ pc), bright ($J< 9$) M- or late K-type dwarf stars, 86% of which have been confirmed by spectroscopy. This catalog will be useful for searches for Earth-size and possibly Earth-like planets by future space-based transit missions and ground-based infrared Doppler radial velocity surveys. Stars were selected from the SUPERBLINK proper motion catalog according to absolute magnitudes, spectra, or a combination of reduced proper motions and photometric colors. From our spectra we determined gravity-sensitive indices, and identified and removed 0.2% of these as interloping hotter or evolved stars. Thirteen percent of the stars exhibit H-alpha emission, an indication of stellar magnetic activity and possible youth. The mean metallicity is [Fe/H] = -0.07 with a standard deviation of 0.22 dex, similar to nearby solar-type stars. We determined stellar effective temperatures by least-squares fitting of spectra to model predictions calibrated by fits to stars with established bolometric temperatures, and estimated radii, luminosities, and masses using empirical relations. Six percent of stars with images from integral field spectra are resolved doubles. We inferred the planet population around M dwarfs using $Kepler$ data and applied this to our catalog to predict detections by future exoplanet surveys.Comment: Accepted to MNRAS 22 figures, 3 tables, 2 electronic tables. Electronic tables are available as links on this pag

Towards Interpretable Deep Learning Models for Knowledge Tracing

As an important technique for modeling the knowledge states of learners, the traditional knowledge tracing (KT) models have been widely used to support intelligent tutoring systems and MOOC platforms. Driven by the fast advancements of deep learning techniques, deep neural network has been recently adopted to design new KT models for achieving better prediction performance. However, the lack of interpretability of these models has painfully impeded their practical applications, as their outputs and working mechanisms suffer from the intransparent decision process and complex inner structures. We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model by backpropagating the relevance from the model's output layer to its input layer. The experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions, and partially validate the computed relevance scores from both question level and concept level. We believe it can be a solid step towards fully interpreting the DLKT models and promote their practical applications in the education domain