Art and Medicine: A Collaborative Project Between Virginia Commonwealth University in Qatar and Weill Cornell Medicine in Qatar

Four faculty researchers, two from Virginia Commonwealth University in Qatar, and two from Weill Cornell Medicine in Qatar developed a one semester workshop-based course in Qatar exploring the connections between art and medicine in a contemporary context. Students (6 art / 6 medicine) were enrolled in the course. The course included presentations by clinicians, medical engineers, artists, computing engineers, an art historian, a graphic designer, a painter, and other experts from the fields of art, design, and medicine. To measure the student experience of interdisciplinarity, the faculty researchers employed a mixed methods approach involving psychometric tests and observational ethnography. Data instruments included pre- and post-course semi-structured audio interviews, pre-test / post-test psychometric instruments (Budner Scale and Torrance Tests of Creativity), observational field notes, self-reflective blogging, and videography. This book describes the course and the experience of the students. It also contains images of the interdisciplinary work they created for a culminating class exhibition. Finally, the book provides insight on how different fields in a Middle Eastern context can share critical /analytical thinking tools to refine their own professional practices

Non-parametric statistical thresholding for sparse magnetoencephalography source reconstructions.

Uncovering brain activity from magnetoencephalography (MEG) data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources. Sparse reconstruction algorithms, such as Champagne, show great promise in that they provide focal brain activations robust to these confounds. In this paper, we address the technical considerations of statistically thresholding brain images obtained from sparse reconstruction algorithms. The source power distribution of sparse algorithms makes this class of algorithms ill-suited to "conventional" techniques. We propose two non-parametric resampling methods hypothesized to be compatible with sparse algorithms. The first adapts the maximal statistic procedure to sparse reconstruction results and the second departs from the maximal statistic, putting forth a less stringent procedure that protects against spurious peaks. Simulated MEG data and three real data sets are utilized to demonstrate the efficacy of the proposed methods. Two sparse algorithms, Champagne and generalized minimum-current estimation (G-MCE), are compared to two non-sparse algorithms, a variant of minimum-norm estimation, sLORETA, and an adaptive beamformer. The results, in general, demonstrate that the already sparse images obtained from Champagne and G-MCE are further thresholded by both proposed statistical thresholding procedures. While non-sparse algorithms are thresholded by the maximal statistic procedure, they are not made sparse. The work presented here is one of the first attempts to address the problem of statistically thresholding sparse reconstructions, and aims to improve upon this already advantageous and powerful class of algorithm

Focal Spot, Spring 2008

https://digitalcommons.wustl.edu/focal_spot_archives/1108/thumbnail.jp

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

Focal Spot, Fall/Winter 2010/2011

https://digitalcommons.wustl.edu/focal_spot_archives/1115/thumbnail.jp