Inference From Aging Information

For many learning tasks the duration of the data collection can be greater than the time scale for changes of the underlying data distribution. The question we ask is how to include the information that data are aging. Ad hoc methods to achieve this include the use of validity windows that prevent the learning machine from making inferences based on old data. This introduces the problem of how to define the size of validity windows. In this brief, a new adaptive Bayesian inspired algorithm is presented for learning drifting concepts. It uses the analogy of validity windows in an adaptive Bayesian way to incorporate changes in the data distribution over time. We apply a theoretical approach based on information geometry to the classification problem and measure its performance in simulations. The uncertainty about the appropriate size of the memory windows is dealt with in a Bayesian manner by integrating over the distribution of the adaptive window size. Thus, the posterior distribution of the weights may develop algebraic tails. The learning algorithm results from tracking the mean and variance of the posterior distribution of the weights. It was found that the algebraic tails of this posterior distribution give the learning algorithm the ability to cope with an evolving environment by permitting the escape from local traps.

Potentially bioavailable metals in sediment from a tropical polymictic environment-Rio Grande Reservoir, Brazil

Background, aim and scope Although many recent studies have focused on sediment potential toxicity, few of them were performed in tropical shallow aquatic environments. Those places can suffer short-time variations, especially due to water column circulations generated by changes in temperature and wind. Rio Grande reservoir is such an example; aside from that, it suffers various anthropogenic impacts, despite its multiple uses. Materials and methods This work presents the first screening step for understanding sediment quality from Rio Grande reservoir by comparing metal content using three different sediment quality guidelines. We also aimed at verifying any possible spatial heterogeneity. Results and discussion We found spatial heterogeneity varying according to the specific metal. Results showed a tendency for metals to remain as insoluble as metal sulfide (potentially not bioavailable), since sulfide was in excess and sediment physical-chemical characteristics contribute to sulfide maintenance (low redox potential, neutral pH, low dissolved oxygen, and high organic matter content). On the other hand, metal concentrations were much higher than suggested by Canadian guidelines and regional background values, especially Cu, which raises the risk of metal remobilization in cases of water circulation. Further study steps include the temporal evaluation of AVS/SEM, a battery of bioassays and the characterization of organic compounds.