James P. Smith
Los Alamos National Laboratory
16 Papers
278 Citations
James P. Smith is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Wireless ad hoc network & Routing protocol. The author has an hindex of 10, co-authored 15 publications.
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Papers
If Smallpox Strikes Portland...
TL;DR: The article looks at "EpiSims," an epidemiology simulation model created to study how social networks spread disease, and smallpox was among the first diseases to model because government officials charged with bioterrorism planning and response were faced with several questions and sometimes conflicting recommendations.
197
Parametric probabilistic sensor network routing
Christopher L. Barrett,Stephan Eidenbenz,Lukas Kroc,Madhav V. Marathe,James P. Smith +4 more
- 19 Sep 2003
TL;DR: The results show that the multi-path protocols are less sensitive to misinformation, and suggest that in the presence of noisy data, a limited flooding strategy will actually perform better and use fewer resources than an attempted single-path routing strategy, with the Parametric Probabilistic Sensor Network Routing Protocols outperforming other protocols.
Semi-empirical power-law scaling of new infection rate to model epidemic dynamics with inhomogeneous mixing.
Phillip D. Stroud,Stephen J. Sydoriak,Jane M. Riese,James P. Smith,Susan M. Mniszewski,Phillip Romero +5 more
TL;DR: The expected number of new infections per day per infectious person during an epidemic has been found to exhibit power-law scaling with respect to the susceptible fraction of the population, in contrast to the linear scaling assumed in traditional epidemiologic modeling.
76
Finding the Number of Latent Topics With Semantic Non-Negative Matrix Factorization
Raviteja Vangara,Manish Bhattarai,Erik Skau,Gopinath Chennupati,Hristo N. Djidjev,Tom Tierney,James P. Smith,Valentin Stanev,Boian S. Alexandrov +8 more
TL;DR: In this paper, a non-negative matrix factorization (NMF) topic model is proposed for text mining, which is based on Kullback-Leibler(KL) divergence and integrated with a method for determining the number of latent topics.
Parametric probabilistic routing in sensor networks
TL;DR: These results for networks with randomly placed nodes and realistic urban networks with varying density show that the multi-path protocols are less sensitive to misinformation, and suggest that in the presence of noisy data, a limited flooding strategy will actually perform better and use fewer resources than an attempted single-path routing strategy, with the Parametric Probabilistic Sensor Network Routing Protocols outperforming other protocols.