A spanning heuristic for the unordered and ordered matching identification problem

The matching identification problem (MIP) is a combinatoric search problem related to the fields of learning from examples, boolean functions, and knowledge acquisition. The MIP involves identifying a single “goal” item from a large set of items. Because there is commonly a cost associated with evaluating each guess, the goal item should be identified in as few guesses as possible. As in most search problems, the items have a similar structure, which allows an evaluation of each guessed item. In other words, each guessed item elicits partial information about the goal item, i.e. how similar the guess is to the goal. With this information the goal is more quickly identified.The unordered MIP has been studied by Mehrez and Steinberg (ORSA J. Comput. 7 (1995) 211) in which they proposed two different types of algorithms. The purpose of the present paper is to suggest an improved Spanning Heuristic algorithm. Its improvement increases as the problem size increases. Further results and comparisons are derived for the unordered and ordered cases.This research shows that when the search space is very large, it is better to inquire from items that are known not to be the goal (they have been ruled out by previous guesses), for the purpose of acquiring more information about the goal. As the search space is narrowed, it is better to guess items that have not been ruled out.     

LC滤波器设计的优化解析公式

给出了改进的Chebyshev和Butterworth低通原型LC滤波器的解析设计公式.此公式充分利用阶数取整带来的余量,在不改变滤波器阶数的前提下减小了通带波动,极大地方便了集成滤波器的实现.给出了集成有源高通滤波器设计实例,验证了改进的Chebyshev和Butterworth低通原型LC滤波器解析设计公式的有效性.     

On Approximability of Boolean Formula Minimization

For a Boolean function given by a Boolean formula (or a binary circuit) S we discuss the problem of building a Boolean formula (binary circuit) of minimal size, which computes the function g equivalent to , or -equivalent to , i.e., . In this paper we prove that if P NP then this problem can not be approximated with a good approximation ratio by a polynomial time algorithm.     

Perfect simulation for correlated Poisson random variables conditioned to be positive

In this paper we present a perfect simulation method for obtaining perfect samples from collections of correlated Poisson random variables conditioned to be positive. We show how to use this method to produce a perfect sample from a Boolean model conditioned to cover a set of points: in W.S. Kendall and E. Thönnes (Pattern Recognition 32(9): 1569–1586, 1999), this special case was treated in a more complicated way. The method is applied to several simple examples where exact calculations can be made, so as to check correctness of the program using ²-tests, and some small-scale experiments are carried out to explore the behaviour of the conditioned Boolean model.     

摆动滚子从动件圆柱凸轮解析设计式的推导

本文较为全面系统地推导了摆动滚子从动件圆柱凸轮的廓线、压力角及廓线曲率半径方程,并讨论了该种凸轮机构在各种运转情况下与之相适用的方程。这些方程可用于凸轮廓线的计算机辅助设计。     

Likelihood Inference for Unions of Interacting Discs

Abstract. This is probably the first paper which discusses likelihood inference for a random set using a germ‐grain model, where the individual grains are unobservable, edge effects occur and other complications appear. We consider the case where the grains form a disc process modelled by a marked point process, where the germs are the centres and the marks are the associated radii of the discs. We propose to use a recent parametric class of interacting disc process models, where the minimal sufficient statistic depends on various geometric properties of the random set, and the density is specified with respect to a given marked Poisson model (i.e. a Boolean model). We show how edge effects and other complications can be handled by considering a certain conditional likelihood. Our methodology is illustrated by analysing Peter Diggle's heather data set, where we discuss the results of simulation‐based maximum likelihood inference and the effect of specifying different reference Poisson models.     

3-状态部分2-树网络可靠度的一个多项式时间算法

本文利用3-状态设备网络可靠度计算归约公式,结合Wald.J.A.-Colbourn C.J.[1]算法(简称为W-C算法2),给出了3-状态部分2-树网络可靠度的一个多项式时间算法.     

The Effects of E-journal Management Tools and Services on Serials Cataloging

Abstract

E-journal management tools and services such as MARC record services, A-to-Z lists, and link resolvers are changing e-journal cataloging. This column explores these changes in the academic environment through interviews with ten librarians representing eight universities. Three areas of change in serials cataloging are explored: (1) changes to the MARC record, including how libraries are adding/creating MARC records for their catalogs, the number and type of MARC records being created and linking within MARC bibliographic and holdings records; (2) the manner in which serials catalogers are being informed of changes; and (3) the evolving role of the serials cataloger. Future trends and advice for evolving workflow practices conclude the discussion.     

Simplicial Pivoting Algorithms for a Tractable Class of Integer Programs

We present a tractable class of integer feasibility problems. This class of max-closed IP problems was studied in somewhat restricted form by Glover, Pnueli, Hochbaum and Chandrasekaran and has a logic counterpart known as the class of Horn formulas. First we modify the existing algorithms in order to avoid the related recognition problem. Then we show that in order to solve these max-closed IP problems, simplicial path following methods can be used. This is important because these methods are flexible with respect to starting conditions, which make them more suitable than the top-down truncation algorithms that have been suggested.     

Genetic programming as a means for programming computers by natural selection

Many seemingly different problems in machine learning, artificial intelligence, and symbolic processing can be viewed as requiring the discovery of a computer program that produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible computer programs for a highly fit individual computer program. The recently developed genetic programming paradigm described herein provides a way to search the space of possible computer programs for a highly fit individual computer program to solve (or approximately solve) a surprising variety of different problems from different fields. In genetic programming, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (sexual recombination) operator appropriate for genetically mating computer programs. Genetic programming is illustrated via an example of machine learning of the Boolean 11-multiplexer function and symbolic regression of the econometric exchange equation from noisy empirical data.Hierarchical automatic function definition enables genetic programming to define potentially useful functions automatically and dynamically during a run, much as a human programmer writing a complex computer program creates subroutines (procedures, functions) to perform groups of steps which must be performed with different instantiations of the dummy variables (formal parameters) in more than one place in the main program. Hierarchical automatic function definition is illustrated via the machine learning of the Boolean 11-parity function.