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James Ford

Researcher at Dartmouth College

Publications -  85
Citations -  2636

James Ford is an academic researcher from Dartmouth College. The author has contributed to research in topics: Wireless sensor network & Negotiation. The author has an hindex of 26, co-authored 84 publications receiving 2438 citations. Previous affiliations of James Ford include University of Texas at Arlington & Dartmouth–Hitchcock Medical Center.

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Proceedings Article

Learning from incomplete ratings using non-negative matrix factorization

TL;DR: Two variations on Non-negative Matrix Factorization (NMF) are introduced: one based on the Expectation-Maximization (EM) procedure and the other a Weighted Nonnegative Matrix factorization (WNMF), which obtain the best prediction performance compared with other popular collaborative filtering algorithms in experiments.

Effect of head impacts on diffusivity measures in a cohort of collegiate contact sport athletes

TL;DR: In this paper, a prospective cohort study at a Division I NCAA athletic program of 80 nonconcussed varsity football and ice hockey players who wore instrumented helmets that recorded the acceleration-time history of the head following impact, and 79 non-contact sport athletes.
Journal ArticleDOI

Effect of head impacts on diffusivity measures in a cohort of collegiate contact sport athletes

TL;DR: A relationship between head impact exposure, white matter diffusion measures, and cognition over the course of a single season, even in the absence of diagnosed concussion, is suggested in a cohort of college athletes.
Journal ArticleDOI

Group-wise evaluation and comparison of white matter fiber strain and maximum principal strain in sports-related concussion.

TL;DR: In this article, the significance of white matter fiber orientation in strain estimation was investigated and compared fiber strain (i.e., axonal elongation as a potential injury mechanism; however, current response-based injury predictors (e.g., maximum principal strain) typically do not incorporate axonal orientations.
Proceedings ArticleDOI

Using singular value decomposition approximation for collaborative filtering

TL;DR: A novel algorithm is presented that incorporates SVD approximation into the EM procedure to reduce the overall computational cost while maintaining accurate predictions, and a new framework for collaborating filtering in distributed recommendation systems that allows users to maintain their own rating profiles for privacy is proposed.