Long range and duration underwater localization using molecular messaging

In this paper, we tackle the problem of how to locate a single entity with an unknown location in a vast underwater search space. In under-water channels, traditional wave-based signals suffer from rapid distance- and time-dependent energy attenuation, leading to expensive and lengthy search missions. In view of this, we investigate two molecular messaging methods for location discovery: a Rosenbrock gradient ascent algorithm, and a chemical encoding messaging method. In absence of explicit diffusion channel knowledge and in presence of diffusion noise, the Rosenbrock method is adapted to account for the blind search process and allow the robot to recover in areas of zero gradient. The two chemical methods are found to offer attractive performance trade-offs in complexity and robustness. Compared to conventional acoustic signals, the chemical methods proposed offers significantly longer propagation distance (1000km) and longer signal persistence duration (months)

Integrating and Ranking Uncertain Scientific Data

Mediator-based data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict less-known or previously unknown functions (though it does not improve predicting the well-known). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for real-world problems as theory indicates