Manifestly Finite Perturbation Theory for the Short-Distance Expansion of Correlation Functions in the Two Dimensional Ising Model

In the spirit of classic works of Wilson on the renormalization group and operator product expansion, a new framework for the study of the theory space of euclidean quantum field theories has been introduced. This formalism is particularly useful for elucidating the structure of the short-distance expansions of the $n$-point functions of a renormalizable quantum field theory near a non-trivial fixed point. We review and apply this formalism in the study of the scaling limit of the two dimensional massive Ising model. Renormalization group analysis and operator product expansions determine all the non-analytic mass dependence of the short-distance expansion of the correlation functions. An extension of the first order variational formula to higher orders provides a manifestly finite scheme for the perturbative calculation of the operator product coefficients to any order in parameters. A perturbative expansion of the correlation functions follows. We implement this scheme for a systematic study of correlation functions involving two spin operators. We show how the necessary non-trivial integrals can be calculated. As two concrete examples we explicitly calculate the short-distance expansion of the spin-spin correlation function to third order and the spin-spin-energy density correlation function to first order in the mass. We also discuss the applicability of our results to perturbations near other non-trivial fixed points corresponding to other unitary minimal models.Comment: 38 pages with 1 figure, UCLA/93/TEP/4

Knowledge Discovery from Users Web-Page Navigation

We propose to detect users navigationpaths to the advantage of web-site owners. First, we explain the design and implementationof a profiler which captures client’s selected links and pages order, accurate page viewing time and cache references, using a Java based remote agent. The information captured by the profiler is then utilized by a knowledge discovery technique to cluster users with similar interests. We introduce a novel path clustering method based on the similarity of the history of user navigation. This approach is capable of capturing the interests of the user which could persist through several subsequent hypertext link selections. Finally, we evaluate our path clustering technique via a simulation study on a sample WWW-site. We show that depending on the level of inserted noise, we can recover the correct clusters by %10-%27 of average error margin. 1

Knowledge Discovery from Users Web-Page Navigation

We propose to detect users navigation paths to the advantage of web-site owners. First, we explain the design and implementation of a profiler which captures client's selected links and pages order, accurate page viewing time and cache references, using a Java based remote agent. The information captured by the profiler is then utilized by a knowledge discovery technique to cluster users with similar interests. We introduce a novel path clustering method based on the similarity of the history of user navigation. This approach is capable of capturing the interests of the user which could persist through several subsequent hypertext link selections. Finally, we evaluate our path clustering technique via a simulation study on a sample WWW-site. We show that depending on the level of inserted noise, we can recover the correct clusters by %10-%27 of average error margin. 1. Introduction The orthodox view on the web-pages assumes a unidirectional flow of information from the server to the cl..

Power and Timing Modeling for ASIC Designs

This paper presents a unified power and timing modeling for ASIC libraries. This ASIC library is being standardized and targeted for a design flow, where timing analysis is complemented by power analysis. We show benchmark results from new industrial gatelevel power analysis tool