Initial formation of the U.S. Geological Survey hydroclimatic data network [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): The U.S. Geological Survey is working to define a hydroclimatic data network. The Geological Survey collects stream discharge data at more than 7000 sites throughout the United States. Many of these stations are operated to supply information about specific activities such as flood control, irrigation projects, or hydropower generation. As a beginning, the Geological Survey will attempt to identify stations that represent natural streamflow. Several lists of stations representing "natural" streamflow have been complied in the past. While there is some overlap among these lists, a consistent compilation is preferred. The present effort is to produce one list identifying those stations having periods of record which would be suitable for mesoscale climatic analyses.

Pixelated liquid-crystal light valve for neural network application.

The liquid-crystal light valve (LCLV) is a useful component for performing integration, thresholding, and gain functions in optical neural networks. Integration of the neural activation channels is implemented by pixelation of the LCLV, with use of a structured metallic layer between the photoconductor and the liquid-crystal layer. Measurements are presented for this type of valve, examples of which were prepared for two specific neural network implementations. The valve fabrication and measurement were carried out at the State Optical Institute, St. Petersburg, Russia, and the modeling and system applications were investigated at the Institute of Microtechnology, Neuchâtel, Switzerland.

Development of low entropy coding in a recurrent network.

In this paper we present an unsupervised neural network which exhibits competition between units via inhibitory feedback. The operation is such as to minimize reconstruction error, both for individual patterns, and over the entire training set. A key difference from networks which perform principal components analysis, or one of its variants, is the ability to converge to non-orthogonal weight values. We discuss the network's operation in relation to the twin goals of maximizing information transfer and minimizing code entropy, and show how the assignment of prior probabilities to network outputs can help to reduce entropy. We present results from two binary coding problems, and from experiments with image coding.