Chaos, but in voting and apportionments?

Mathematical chaos and related concepts are used to explain and resolve issues ranging from voting paradoxes to the apportioning of congressional seats.

Elections, information aggregation, and strategic voting

A central role of elections is the aggregation of information dispersed within a population. This article surveys recent work on elections as mechanisms for aggregating information and on the incentives for voters to vote strategically in such elections.

New Members and Foreign Associates Elected to the National Academy of Sciences on May 1, 2001: 72 New Members Chosen by the Academy

The Academy has elected 72 new members and 15 foreign associates from 10 countries in recognition of their distinguished and continuing achievements in original research. The election was held during the business session of the 138th annual meeting of the Academy. Election to membership in the Academy is considered one of the highest honors that can be accorded a U.S. scientist or engineer. Foreign associates are non-voting members of the Academy, with citizenship outside of the United States.

New Members and Foreign Associates Elected to the National Academy of Sciences on May 2, 2000: 60 New Members Chosen by the Academy

The Academy has elected 60 new members and 15 foreign associates from 9 countries in recognition of their distinguished and continuing achievements in original research. The election was held during the business session of the 137th annual meeting of the Academy. Election to membership in the Academy is considered one of the highest honors that can be accorded a U.S. scientist or engineer. Foreign associates are non-voting members of the Academy, with citizenship outside of the United States.

Prediction of protein folding class using global description of amino acid sequence.

We present a method for predicting protein folding class based on global protein chain description and a voting process. Selection of the best descriptors was achieved by a computer-simulated neural network trained on a data base consisting of 83 folding classes. Protein-chain descriptors include overall composition, transition, and distribution of amino acid attributes, such as relative hydrophobicity, predicted secondary structure, and predicted solvent exposure. Cross-validation testing was performed on 15 of the largest classes. The test shows that proteins were assigned to the correct class (correct positive prediction) with an average accuracy of 71.7%, whereas the inverse prediction of proteins as not belonging to a particular class (correct negative prediction) was 90-95% accurate. When tested on 254 structures used in this study, the top two predictions contained the correct class in 91% of the cases.