Monte Carlo Methods for 2-D Compaction

A new method of compaction for VLSI circuits is presented. Compaction is done simultaneously in two dimensions and uses a Monte Carlo simulation method often referred to as simulated annealing for optimization. A new curvilinear representation for VLSI circuits, specifically chosen to make the compaction efficient, is developed. Experiments with numerous cells are presented that demonstrate this method to be as good as, or better than the hand compaction previously applied to these cells. Hand compaction was the best previously known method of compaction. An experimental evaluation is presented of how the run time complexity grows as the number, N, of objects in the circuit increases. The results of this evaluation indicates that the run time growth is order O(N log(A))f(d) where f(d) is a function of the density, d, and A is the initial cell area. The function f(d) appears to have negligible or no dependence on N. A hierarchical composition approach is developed which takes advantage of the capability of the curvilinear representation and the 2- dimensional compaction technique

Deviations from 1:1 and numbers of progeny necessary for establishing linkage

We have found the following table useful when assigning new genes to linkage groups, either with Parental:Recombinant random isolates or with Parental ditype:Nonparental ditype tetrads. The ratios for total numbers through 50 are from Perkins, D.D. 1953 Genetics 38:187-197, and were extracted from tables in Warwick, B.L. Texas Agric. Exper. Sta. Bull. 463:1-28. The ratios for total numbers from 51-100 were obtained using binomial probability paper (Mosteller, F., and J.W. Tukey. J. Amer. Stat. Assoc. 44:174-212)

Making The Abolition of Man Accessible

A review of Tim Mosteller and Gayne John Anacker, eds. Contemporary Perspectives on C. S. Lewis’s The Abolition of Man: History, Philosophy, Education, and Science. (London, UK: Bloomsbury, 2017). $75.00. 176 pages. ISBN 9781474269441

Decision Analysis and FDA Drug Review: A Proposal for Shadow Advisory Committees

The FDA seems to acknowledge that sometimes different standards of proof for assessing drug efficacy should be used. Dr. Mendeloff thus proposes a methodology that might illuminate the FDA\u27s decision-making and help better to assess its decisions

The Role and Responsibility of Prosecutors

In this panel, the discussion moves form the abstract to the very specific. Moderator Thomas Metzloff, professor of law at Duke University School of Law, asks the panelists to analyze the actions leading to the disbarment of former Durham District Attorney Mike Nifong. The speakers, all current or former prosecutors, detail personal experiences dealing with reporters, and discuss the guidelines, written and unwritten, which govern such interaction. Questions/themes/discussion topics The press as a tool to communicate with the public The press as a watchdog Training of young prosecutors in press interaction The effect of the U.S. Supreme Court\u27s 2002 decision in Minnesota v. White on regulating attorney speech Strategic advantages (or lack thereof) to press interaction To what degree can prosecutors control leaks from their office or, more problematically, from the local police department? Public statements as jury pool pollutant

Classification

In Classification learning, an algorithm is presented with a set of classified examples or ‘‘instances’’ from which it is expected to infer a way of classifying unseen instances into one of several ‘‘classes’’. Instances have a set of features or ‘‘attributes’’ whose values define that particular instance. Numeric prediction, or ‘‘regression,’’ is a variant of classification learning in which the class attribute is numeric rather than categorical. Classification learning is sometimes called supervised because the method operates under supervision by being provided with the actual outcome for each of the training instances. This contrasts with Data clustering (see entry Data Clustering), where the classes are not given, and with Association learning (see entry Association Learning), which seeks any association – not just one that predicts the class