Excerpt from Some Opinions and Papers of Stephen J. Field, Vol. 1: Associate Justice and Chief Justice of the Supreme Court of California, United States Circuit Justice for the Ninth and Tenth Circuits, and Associate Justice of the Supreme Court of the United States Fine and imprisonment are prescribed as a punishment for holding or exercising any of the Offices, positions, trusts, pro fessions, or functions specified, without having taken the oath; and false swearing or affirmation in taking it is declared to be perjury punishable by imprisonment in the penitentiary. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.

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This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the "public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.

"Among the judicial immigrants ... were the southerner David S. Terry of Texas and the northerner Stephen J. Field of New York. These men served on California's highest court during its formative, strenuous years from 1855 to 1863. ... The intellectual similarities and differences that these two shared ... played themselves out over a period of 35 years and brought about a series of events that neither man could have envisioned. Their exchanges began as wary judicial amity within the courtroom, but in short order spilled out into the community as public grudges. Neither judge could tolerate the other's regional provincialism; hence, lifelong resentments inevitably turned into a bitterness that led to tragedy"--Foreword, p. vii.

The motivation for the research that is described in these volumes is the wish to explain things in terms of their underlying causes, rather than merely being satisfied with phenomenological descriptions. When this reductionist approach is applied to information processing it allows the internal structure of information to be analysed, so information processing algorithms can then be derived from first principles. One of the simplest examples of this approach is the diagonalisation of a data covariance matrix – there are many variants of this basic approach, such as singular value decomposition – in which the assumed independent components of high-dimensional data are identified and extracted. The main limitation of this type of information analysis approach is that it is based on linear algebra applied globally to the data space, so it is unable to preserve information about any local data structure in the data space. For instance, if the data lives on a low-dimensional curved manifold embedded in the data space, then only the global properties of this manifold would be preserved by global linear algebra methods. In practice, data whose high-dimensional structure is non-trivial typically lives on a noisy version of a curved manifold, so techniques for analysing such data must automatically handle this type of structure. For instance, a blurred image of a point source is described by its underlying degrees of freedom – i.e. the position of the source – and as the source moves about it generates a curved manifold that lives in the high-dimensional space of pixel values of the sampled image. The basic problem is then to deduce the internal properties of this manifold by analysing examples of such images. A more challenging problem would be to extend this analysis to images that contain several overlapping blurred images of point sources, and so on. There is no limit to the complexity of the types of high-dimensional data that one might want to analyse. These methods then need to be automated so that they do not rely on human intervention, which would then allow them to be inserted as “components” into information processing networks. The purpose of the research that is described in these volumes is to develop principled information processing methods that can be used for such analysis. Self-organising information processing networks arise naturally in this context, in which ways of cutting up the original manifold into simpler pieces emerge automatically.