Summary form only given. An application program interface (API) for meddling sequential text is described. The API is intended to shield the user from details of the modelling and probability estimation process. This should enable different implementations of models to be replaced transparently in application programs. The motivation for this API is work on the use of textual models for applications in addition to strict data compression. The API is probabilistic, that is, it supplies the probability of the next symbol in the sequence. It is general enough to deal accurately with models that include escapes for probabilities. The concepts abstracted by the API are explained together with details of the API calls. Such predictive models can be used for a number of applications other than compression. Users of the models do not want to be concerned about the details either of the implementation of the models or how they were trained and the sources of the training text. The problem considered is how to permit code for different models and actual trained models themselves to be interchanged easily between users. The fundamental idea is that it should be possible to write application programs independent of the details of particular modelling code, that it should be possible to implement different modelling code independent of the various applications, and that it should be possible to easily exchange different pre-trained models between users. It is hoped that this independence will foster the exchange and use of high-performance modelling code, the construction of sophisticated adaptive systems based on the best available models, and the proliferation and provision of high-quality models of standard text types such as English or other natural languages, and easy comparison of different modelling techniques.

The problem of compressing English text is important both because of the ubiquity of English as a target for compression and because of the light that compression can shed on the structure of English. English text is examined in conjunction with additional information about the parts of speech of each word in the text (these are referred to as "tags"). It is shown that the tags plus the text can be compressed more than the text alone. Essentially the tags can be compressed for nothing or even a small net saving in size. A comparison is made of a number of different ways of integrating compression of tags and text using an escape mechanism similar to PPM. These are also compared with standard word based and character based compression programs. The result is that the tag and word based schemes always outperform the character based schemes. Overall, the tag based schemes outperform the word based schemes. We conclude by conjecturing that tags chosen for compression rather than linguistic purposes would perform even better.

An essential component of many applications in natural language processing is a language modeler able to correct errors in the text being processed. For optical character recognition (OCR), poor scanning quality or extraneous pixels in the image may cause one or more characters to be mis-recognized, while for spelling correction, two characters may be transposed, or a character may be inadvertently inserted or missed out, This paper describes a method for correcting English text using a PPM model. A method that segments words in English text is introduced and is shown to be a significant improvement over previously used methods. A similar technique is also applied as a post-processing stage after pages have been recognized by a state-of-the-art commercial OCR system. We show that the accuracy of the OCR system can be increased from 96.3% to 96.9%, a decrease of about 14 errors per page.

The problem of constructing models of English text is considered. A number of applications of such models including cryptology, spelling correction and speech recognition are reviewed. The best current models for English text have been the result of research into compression. Not only is this an important application of such models but the amount of compression provides a measure of how well such models perform. Three main classes of models are considered: character based models, word based models, and models which use auxiliary information in the form of parts of speech. These models are compared in terms of their memory usage and compression.

This paper exemplifies the similarities in Time Warp and computer architecture concepts and terminology, and the continued trend for convergence of ideas in these two areas. Time Warp can provide a means to describe the complex mechanisms being used to allow the instruction execution window to be enlarged. Furthermore it can extend the current mechanisms, which do not scale, in a scalable manner. The issues involved in implementing Time Warp in a CPU design are also examined, and illustrated with reference to the Wisconsin Multiscalar machine and the Waikato WarpEngine. Finally the potential performance gains of such a system are briefly discussed.

Arithmetic coding for data compression has gained widespread acceptance as the right method for optimum compression when used with a suitable source model. A technique to implement error detection as part of the arithmetic coding process is described. Heuristic arguments are given to show that a small amount of extra redundancy can be very effective in detecting errors very quickly, and practical tests confirm this prediction.

The purpose of this paper is to show that the difference between the best machine models and human models is smaller than might be indicated by the previous results. This follows from a number of observations: firstly, the original human experiments used only 27 character English (letters plus space) against full 128 character ASCII text for most computer experiments; secondly, using large amounts of priming text substantially improves the PPM's performance; and thirdly, the PPM algorithm can be modified to perform better for English text. The result of this is a machine performance down to 1.46 bit per character. The problem of estimating the entropy of English is discussed. The importance of training text for PPM is demonstrated, showing that its performance can be improved by "adjusting" the alphabet used. The results based on these improvements are then given, with compression down to 1.46 bpc.

The prediction by partial matching (PPM) data compression scheme has set the performance standard in lossless compression of text throughout the past decade. The original algorithm was first published in 1984 by Cleary and Witten, and a series of improvements was described by Moffat (1990), culminating in a careful implementation, called PPMC, which has become the benchmark version. This still achieves results superior to virtually all other compression methods, despite many attempts to better it. PPM, is a finite-context statistical modeling technique that can be viewed as blending together several fixed-order context models to predict the next character in the input sequence. Prediction probabilities for each context in the model are calculated from frequency counts which are updated adaptively; and the symbol that actually occurs is encoded relative to its predicted distribution using arithmetic coding. The paper describes a new algorithm, PPM*, which exploits contexts of unbounded length. It reliably achieves compression superior to PPMC, although our current implementation uses considerably greater computational resources (both time and space). The basic PPM compression scheme is described, showing the use of contexts of unbounded length, and how it can be implemented using a tree data structure. Some results are given that demonstrate an improvement of about 6% over the old method.

The feature article addresses issues that are now of crucial importance to this field. Model development for efficient parallel execution is too difficult. Our initial goal at Jade was to create a parallel simulation environment that could be used to solve a wide variety of practical problems. Fujimoto's paper outlines many of the reasons why we have not yet achieved this goal. Our experience at Jade supports Fujimoto's statement that it typically takes “highly skilled experts” to develop models for real applications that can achieve significant speedup due to parallelism. Most of the reasons he presents for this unfortunate situation also agree with our experience. However, we believe there are some important differences in how these difficulties can be addressed. An approach to the design of efficient parallel models is sketched below. This discussion identifies four key indicators of performance and suggests adding one more silver bullet to Fujimoto's list: tools that support the incremental development of efficient models. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Abstract This paper shows how trees can be stored in a very compact form, called ‘Bonsai’, using hash tables. A method is described that is suitable for large trees that grow monotonically within a predefined maximum size limit. Using it, pointers in any tree can be represented within 6 + [log2n] bits per node where n is the maximum number of children a node can have. We first describe a general way of storing trees in hash tables, and then introduce the idea of compact hashing which underlies the Bonsai structure. These two techniques are combined to give a compact representation of trees, and a practical methodology is set out to permit the design of these structures. The new representation is compared with two conventional tree implementations in terms of the storage required per node. Examples of programs that must store large trees within a strict maximum size include those that operate on trie structures derived from natural language text. We describe how the Bonsai technique has been applied to the trees that arise in text compression and adaptive prediction, and include a discussion of the design parameters that work well in practice.

Abstract A multiprocessor algorithm for ray tracing is described. The performance of the algorithm is analysed for a cubic and square array of processors with only local communication between near neighbours. Theoretical expressions for the speedup of the system as a function of the number of processors are derived. These analytic results are supported by simulations of ray tracing on a number of simple scenes with polygonal surfaces. It is found that a square network of processors generally performs better than a cubic network. Some comments are made on the construction of such a system using current (1985) microprocessor technology.

An algorithm is developed which reduces the memory requirements of hash tables. This is achieved by storing only a part of each key along with a few extra bits needed to ensure that all keys are stored unambiguously. The fraction of each key stored decreases as the size of the hash table increases. Significant reductions in total memory usage can be achieved especially when the key size is not much larger than the size of a memory index and when only a small amount of data is stored with each key. The algorithm is based on bidirectional linear probing. Search and insertion times are shown by simulation to be similar to those for ordinary bidirectional linear probing.

The recently developed technique of arithmetic coding, in conjunction with a Markov model of the source, is a powerful method of data compression in situations where a linear treatment is inappropriate. Adaptive coding allows the model to be constructed dynamically by both encoder and decoder during the course of the transmission, and has been shown to incur a smaller coding overhead than explicit transmission of the model's statistics. But there is a basic conflict between the desire to use high-order Markov models and the need to have them formed quickly as the initial part of the message is sent. This paper describes how the conflict can be resolved with partial string matching, and reports experimental results which show that mixed-case English text can be coded in as little as 2.2 bits/ character with no prior knowledge of the source.

The problem of noiselessly encoding a message when prior statistics are known is considered. The close relationship of arithmetic and enumerative coding for this problem is shown by computing explicit arithmetic coding probabilities for various enumerative coding examples. This enables a comparison to be made of the coding efficiency of Markov models and enumerative codes as well as a new coding scheme intermediate between the two. These codes are then extended to messages whose statistics are not known {\em a priori} Two adaptive codes are described for this problem whose coding efficiency is upper-bounded by the extended enumerative codes. On some practical examples the adaptive codes perform significantly better than the nonadaptive ones.

article Analysis of an Algorithm for Finding Nearest Neighbors in Euclidean Space Share on Author: John Gerald Cleary University of Canterbury, Christchurch 1, New Zealand University of Canterbury, Christchurch 1, New ZealandView Profile Authors Info & Claims ACM Transactions on Mathematical SoftwareVolume 5Issue 2June 1979 pp 183–192https://doi.org/10.1145/355826.355832Online:01 June 1979Publication History 41citation850DownloadsMetricsTotal Citations41Total Downloads850Last 12 Months8Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access