Forecasting Global Temperature Variations by Neural Networks

Global temperature variations between 1861 and 1984 are forecast usingsregularization networks, multilayer perceptrons and linearsautoregression. The regularization network, optimized by stochasticsgradient descent associated with colored noise, gives the bestsforecasts. For all the models, prediction errors noticeably increasesafter 1965. These results are consistent with the hypothesis that thesclimate dynamics is characterized by low-dimensional chaos and thatsthe it may have changed at some point after 1965, which is alsosconsistent with the recent idea of climate change.s

Belief Propagation and Revision in Networks with Loops

Local belief propagation rules of the sort proposed by Pearl(1988) are guaranteed to converge to the optimal beliefs for singly connected networks. Recently, a number of researchers have empirically demonstrated good performance of these same algorithms on networks with loops, but a theoretical understanding of this performance has yet to be achieved. Here we lay the foundation for an understanding of belief propagation in networks with loops. For networks with a single loop, we derive ananalytical relationship between the steady state beliefs in the loopy network and the true posterior probability. Using this relationship we show a category of networks for which the MAP estimate obtained by belief update and by belief revision can be proven to be optimal (although the beliefs will be incorrect). We show how nodes can use local information in the messages they receive in order to correct the steady state beliefs. Furthermore we prove that for all networks with a single loop, the MAP estimate obtained by belief revisionat convergence is guaranteed to give the globally optimal sequence of states. The result is independent of the length of the cycle and the size of the statespace. For networks with multiple loops, we introduce the concept of a "balanced network" and show simulati.

Synthesis, Optical properties and Applications of Water Soluble Conjugated PPPs for Biosensors

In recent years, application of fluorescent conjugated polymers to sense chemical and biological analytes has received much attention owing to its technological significance. Water soluble conjugated polymers are interesting towards the developing sensors for biomolecules. In this present contribution, we describe the syntheses and characterization of a series of water soluble conjugated polymers with sulfonic acid groups in the side chain. Such anionic conjugated polymers are designed to interact with biomolecules such as cytochrome-C. All polymers are water soluble and showed strong blue emission. Significant quenching of the fluorescence from our functionalized PPP was observed upon addition of viologen derivatives or cytochrome -C.