Economic Statistics and U.S. Agricultural Policy

Economic statistics can be used to inform policy as it is being designed, avoid policy design mistakes, or implement government programs once they are established into law. Oftentimes, statistics are used for all three purposes. This paper considers the relationships between statistics and agricultural policy in the case of the United States. We address first the broad historical picture of U.S. official economic statistics concerning agriculture, and then turn to selected examples that relate policies to economic statistics in more detail. The examples show diversity in the interplay between statistics and policy. As policies have become broader in scope, addressing not only farm commodity markets but also differences among farms and a widening set of activities on farms, policymakers have asked for more detailed information about the financial situation of individual farm businesses and households, sources of risk in farm returns, and production practices that affect the environment.Agricultural policy, Data collection and estimation, Economic history of U.S. agriculture, Agricultural and Food Policy, Q18, C8, N52,

Surveys of New Mexico Alfalfa Producers and Dairy Hay Users: Will Growth of the State's Dairy Industry be Limited by Alfalfa Availability?

Production Economics,

Optimally adapted multi-state neural networks trained with noise

The principle of adaptation in a noisy retrieval environment is extended here to a diluted attractor neural network of Q-state neurons trained with noisy data. The network is adapted to an appropriate noisy training overlap and training activity which are determined self-consistently by the optimized retrieval attractor overlap and activity. The optimized storage capacity and the corresponding retriever overlap are considerably enhanced by an adequate threshold in the states. Explicit results for improved optimal performance and new retriever phase diagrams are obtained for Q=3 and Q=4, with coexisting phases over a wide range of thresholds. Most of the interesting results are stable to replica-symmetry-breaking fluctuations.Comment: 22 pages, 5 figures, accepted for publication in PR

A polynomial training algorithm for calculating perceptrons of optimal stability

Recomi (REpeated COrrelation Matrix Inversion) is a polynomially fast algorithm for searching optimally stable solutions of the perceptron learning problem. For random unbiased and biased patterns it is shown that the algorithm is able to find optimal solutions, if any exist, in at worst O(N^4) floating point operations. Even beyond the critical storage capacity alpha_c the algorithm is able to find locally stable solutions (with negative stability) at the same speed. There are no divergent time scales in the learning process. A full proof of convergence cannot yet be given, only major constituents of a proof are shown.Comment: 11 pages, Latex, 4 EPS figure

Daytime lidar measurements of tidal winds in the mesospheric sodium layer at Urbana, Illinois

For more than 15 years lidar systems have been used to study the chemistry and dynamics of the mesospheric sodium layer. Because the layer is an excellent tracer of atmospheric wave motions, sodium lidar has proven to be particularly useful for studying the influence of gravity waves and tides on mesospheric dynamics. These waves, which originate in the troposphere and stratosphere, propagate through the mesosphere and dissipate their energy near the mesopause making important contributions to the momentum and turbulence budget in this region of the atmosphere. Recently, the sodium lidar was modified for daytime operation so that wave phenomena and chemical effects could be monitored throughout the complete diurnal cycle. The results of continuous 24 hour lidar observations of the sodium layer structure are presented alond with measurement of the semidiurnal tidal winds

A Content Based Pattern Analysis System for a Biological Specimen Collection

Over the years many research collections of biological specimen have been developed for research in biological sciences. Number of specimens in some of these collections can be as high as several millions. There is a move to convert these physical specimens into digital images. This research is motivated by the need to develop techniques to mine useful information from these large collections of specimen images. Specific focus of this research is on the collection of parasites in the Harold W. Manter Laboratory (HWML) Parasite Collection, one of the top four parasite collections in the world. These parasites closely resemble in shape and have flexible bodies with rigid extremities. They have only a few specific structural differences. In this paper we present a technique to retrieve specimens based on shape of a given sample. This form of mining based on the shape of the specimen has the potential to discover linkages between specimens not otherwise known

Parisi Phase in a Neuron

Pattern storage by a single neuron is revisited. Generalizing Parisi's framework for spin glasses we obtain a variational free energy functional for the neuron. The solution is demonstrated at high temperature and large relative number of examples, where several phases are identified by thermodynamical stability analysis, two of them exhibiting spontaneous full replica symmetry breaking. We give analytically the curved segments of the order parameter function and in representative cases compute the free energy, the storage error, and the entropy.Comment: 4 pages in prl twocolumn format + 3 Postscript figures. Submitted to Physical Review Letter

Information extraction and transmission techniques for spaceborne synthetic aperture radar images

Information extraction and transmission techniques for synthetic aperture radar (SAR) imagery were investigated. Four interrelated problems were addressed. An optimal tonal SAR image classification algorithm was developed and evaluated. A data compression technique was developed for SAR imagery which is simple and provides a 5:1 compression with acceptable image quality. An optimal textural edge detector was developed. Several SAR image enhancement algorithms have been proposed. The effectiveness of each algorithm was compared quantitatively

Storage capacity of a constructive learning algorithm

Upper and lower bounds for the typical storage capacity of a constructive algorithm, the Tilinglike Learning Algorithm for the Parity Machine [M. Biehl and M. Opper, Phys. Rev. A {\bf 44} 6888 (1991)], are determined in the asymptotic limit of large training set sizes. The properties of a perceptron with threshold, learning a training set of patterns having a biased distribution of targets, needed as an intermediate step in the capacity calculation, are determined analytically. The lower bound for the capacity, determined with a cavity method, is proportional to the number of hidden units. The upper bound, obtained with the hypothesis of replica symmetry, is close to the one predicted by Mitchinson and Durbin [Biol. Cyber. {\bf 60} 345 (1989)].Comment: 13 pages, 1 figur