Browsing by Author "Mukherjee, S"

Now showing 1 - 8 of 8

Open Access
A Cheeger-type inequality on simplicial complexes
(ADVANCES IN APPLIED MATHEMATICS, 2014-05) Steenbergen, J; Klivans, C; Mukherjee, S
Open Access
Approximations of Markov Chains and High-Dimensional Bayesian Inference
(2015) Mattingly, JC; Johndrow, J; Mukherjee, S; Dunson, D
Open Access
Citizen Science as a New Tool in Dog Cognition Research.
(PLoS One, 2015) Stewart, L; MacLean, EL; Ivy, D; Woods, V; Cohen, E; Rodriguez, K; McIntyre, M; Mukherjee, S; Call, J; Kaminski, J; Miklósi, Á; Wrangham, RW; Hare, B
Family dogs and dog owners offer a potentially powerful way to conduct citizen science to answer questions about animal behavior that are difficult to answer with more conventional approaches. Here we evaluate the quality of the first data on dog cognition collected by citizen scientists using the Dognition.com website. We conducted analyses to understand if data generated by over 500 citizen scientists replicates internally and in comparison to previously published findings. Half of participants participated for free while the other half paid for access. The website provided each participant a temperament questionnaire and instructions on how to conduct a series of ten cognitive tests. Participation required internet access, a dog and some common household items. Participants could record their responses on any PC, tablet or smartphone from anywhere in the world and data were retained on servers. Results from citizen scientists and their dogs replicated a number of previously described phenomena from conventional lab-based research. There was little evidence that citizen scientists manipulated their results. To illustrate the potential uses of relatively large samples of citizen science data, we then used factor analysis to examine individual differences across the cognitive tasks. The data were best explained by multiple factors in support of the hypothesis that nonhumans, including dogs, can evolve multiple cognitive domains that vary independently. This analysis suggests that in the future, citizen scientists will generate useful datasets that test hypotheses and answer questions as a complement to conventional laboratory techniques used to study dog psychology.
Open Access
CONSISTENCY OF MAXIMUM LIKELIHOOD ESTIMATION FOR SOME DYNAMICAL SYSTEMS
(ANNALS OF STATISTICS, 2015-02) McGoff, K; Mukherjee, S; Nobel, A; Pillai, N
Open Access
Dorsal thoracic arachnoid web and the "scalpel sign": a distinct clinical-radiologic entity.
(AJNR. American journal of neuroradiology, 2013-05) Reardon, MA; Raghavan, P; Carpenter-Bailey, K; Mukherjee, S; Smith, JS; Matsumoto, JA; Yen, C-P; Shaffrey, ME; Lee, RR; Shaffrey, CI; Wintermark, M
Arachnoid webs are intradural extramedullary bands of arachnoid tissue that can extend to the pial surface of the spinal cord, causing a focal dorsal indentation of the cord. These webs tend to occur in the upper thoracic spine and may produce a characteristic deformity of the cord that we term the "scalpel sign." We describe 14 patients whose imaging studies demonstrated the scalpel sign. Ten of 13 patients who underwent MR imaging demonstrated T2WI cord signal-intensity changes, and 7 of these patients also demonstrated syringomyelia adjacent to the level of indentation. Seven patients underwent surgery, with 5 demonstrating an arachnoid web as the cause of the dorsal indentation demonstrated on preoperative imaging. Although the webs themselves are rarely demonstrated on imaging, we propose that the scalpel sign is a reliable indicator of their presence and should prompt consideration of surgical lysis, which is potentially curative.
Open Access
Fréchet Means for Distributions of Persistence Diagrams
(Discrete & Computational Geometry, 2014) Turner, K; Mileyko, Y; Mukherjee, S; Harer, J
Open Access
Learning gradients on manifolds
(BERNOULLI, 2010-02) Mukherjee, S; Wu, Q; Zhou, DX
Open Access
Learning gradients: Predictive models that infer geometry and statistical dependence
(Journal of Machine Learning Research, 2010-08-01) Wu, Q; Guinney, J; Maggioni, M; Mukherjee, S
The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean spaces as well as the manifold setting. The central quantity in this approach is an estimate of the gradient of the regression or classification function. Two quadratic forms are constructed from gradient estimates: the gradient outer product and gradient based diffusion maps. The first quantity can be used for supervised dimension reduction on manifolds as well as inference of a graphical model encoding dependencies that are predictive of a response variable. The second quantity can be used for nonlinear projections that incorporate both the geometric structure of the manifold as well as variation of the response variable on the manifold. We relate the gradient outer product to standard statistical quantities such as covariances and provide a simple and precise comparison of a variety of supervised dimensionality reduction methods. We provide rates of convergence for both inference of informative directions as well as inference of a graphical model of variable dependencies. © 2010.