Epistemic Bubbles in Affluent Schools

This essay explores the teachings of Dr. Adam Howard, an educator and researcher focused on the relationship between privilege and identity in educational systems, through the lens of the epistemic bubble. It reviews what epistemic bubbles are, how they are formed, and how and why we should combat them, drawing from Dr. Howard’s experiences with similar structures in affluent schooling

Graduate Recital: Kyle Pieczynski

Kemp Recital HallApril 29, 2012Sunday Afternoon5:00 p.m

Lower limb locomotion activity recognition of healthy individuals using semi-Markov model and single wearable inertial sensor

International audienceLower limb locomotion activity is of great interest in the field of human activity recognition. In this work, a triplet semi-Markov model-based method is proposed to recognize the locomotion activities of healthy individuals when lower limbs move periodically. In the proposed algorithm, the gait phases (or leg phases) are introduced into the hidden states, and Gaussian mixture density is introduced to represent the complex conditioned observation density. The introduced sojourn state forms the semi-Markov structure, which naturally replicates the real transition of activity and gait during motion. Then, batch mode and on-line Expectation-Maximization (EM) algorithms are proposed, respectively, for model training and adaptive on-line recognition. The algorithm is tested on two datasets collected from wearable inertial sensors. The batch mode recognition accuracy reaches up to 95.16%, whereas the adaptive on-line recognition gradually obtains high accuracy after the time required for model updating. Experimental results show an improvement in performance compared to the other competitive algorithm

Fast exact filtering in generalized conditionally observed Markov switching models with copulas

International audienceWe deal with the problem of statistical filtering in the context of Markov switching models. For X_1^N hidden continuous process, R_1^N hidden finite Markov process, and Y_1^N observed continuous one, the problem is to sequentially estimate X_1^N and R_1^N from Y_1^N. In the classical " conditional Gaussian Linear state space model " (CGLSSM), where (R_1^N, X_1^N) is a hidden Gaussian Markov chain, fast exact filtering is not workable. Recently, " conditionally Gaussian observed Markov switching model " (CGOMSM) has been proposed, in which (R_1^N, Y_1^N) is a hidden Gaussian Markov chain instead. This model allows fast exact filtering. In this paper, using copula, we extend CGOMSM to a more general one, in which (R_1^N, Y_1^N) is a hidden Markov chain (HMC) with noise of any form and the regimes are no need to be all Gaussian, while the exact filtering is still workable. Experiments are conducted to show how the exact filtering results based on CGOMSM can be improved by the use of the new model

Joint Recital: Steve Kowaleski, Guitar; Kyle Pieczynski, Guitar; April 16, 2010

Kemp Recital HallApril 16, 2010Friday Evening7:00 p.m

Peer tutoring and academic integrity in multicultural context

It has long been common practice for academics to ask colleagues for feedback on their articles when drafting. This practice can be beneficial for both reader and writer, who, by creating a conversation about ideas and their expression, may come closer to an accurate description of their research

Sur la convergence de l'estimation conditionnelle itérative

International audienceThe iterative conditional estimation (ICE) is an iterative estimation method of the parameters in the case of incomplete data. Its use asks for relatively weak hypotheses and it can be performed in relatively complex situations, as in triplet Markov models. The aim of this Note is to express a general theorem of convergence of ICE, and to show its applicability in the problem of the estimation of the proportions in a mixture of multivariate distributions.L'estimation conditionnelle itérative (ECI) est une méthode d'estimation itérative des paramètres dans le cas des données incomplètes. Sa mise en oeuvre demande des hypothèses relativement faibles et peut être effectuée dans des situations relativement complexes, comme les champs de Markov cachés à états mixtes. Proposée il y a une quinzaine d'années, l'ECI a été appliquée avec succès aux différents problèmes de segmentation bayésienne non supervisée d'images et des signaux ; cependant, aucun résultat théorique n'est venu étayer ce bon comportement. L'objet de cette note est d'énoncer un théorème général de convergence de l'ECI, et de montrer son applicabilité dans le problème de l'estimation de la proportion dans un mélange de lois multi-variée