Entropy and inference, revisited

We study properties of popular near-uniform (Dirichlet) priors for learning undersampled probability distributions on discrete nonmetric spaces and show that they lead to disastrous results. However, an Occam-style phase space argument expands the priors into their infinite mixture and resolves most of the observed problems. This leads to a surprisingly good estimator of entropies of discrete distributions.Comment: LaTex2e, 9 pages, 5 figures; references added, minor revisions introduced, formatting errors correcte

Theory-driven learning : using intra-example relationships to constrain learning

We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of regularities among rules to constrain the learning problem. We demonstrate that this knowledge enables the learning system to rapidly converge on accurate predictive rules and to tolerate more complex training data. An algorithm for incrementally learning these regularities is described and we provide evidence that the resulting regularities are sufficiently general to facilitate learning in new domains

A method to generate occam skeletons from formal specifications

We consider the problem of automatically generating parallel programs in the occam language. In particular, we are concerned with the generation of program skeletons, that define just those computations that are common to a number of similar systems leaving empty or 'stub' functions or processes in the code which are to be filled in later in an application-specific manner. Rather than attempt to solve the problem for arbitrary parallel systems, we focus on data-parallel systems composed of iterative processes. In this paper, we propose an implementation strategy for a cellular automaton. Only uniprocessor occam code is considered

Literature Review For Networking And Communication Technology

Report documents the results of a literature search performed in the area of networking and communication technology

Capstan drive transport system for motion picture film

The work presented describes the development of a capstan drive system for the transport of motion picture film. From a model description of the plant and computer aided system design analysis, control algorithms are formulated. The work shows how these relativity complex control algorithms are implemented by making use of the parallel processing capabilities of the transputer. A critical investigation of current film transport methods is undertaken leading to the design and testing of a prototype capstan drive mechanism. The capstan drive system is shown to eliminate problems of sprocket drives and their associated mechanisms. A multi-input multi-output controller is presented using state-space methods of design. The developed control strategies are fully tested on a model of the plant before hardware testing. The control outputs of the system are speed and tension. The final control solution is shown to be a combination of full-state feedback, integral control, and a Kalman filter estimator for the elimination of system disturbances. The transputer implementation of the developed control strategies is presented together with a comparison between simulation and experimental results. It is shown that computational times can be reduced by using multiple transputers and placing computation-intensive sections of the control algorithm on separate processors. Transputer configurations and interconnections are shown. The capstan system has been shown to allow faster printing speeds with improved transport accuracy leading to better quality of the final picture print. The system has been shown to be 'robust' to external disturbances and changes in plant parameters

Probabilistic Graphical Models on Multi-Core CPUs using Java 8

In this paper, we discuss software design issues related to the development of parallel computational intelligence algorithms on multi-core CPUs, using the new Java 8 functional programming features. In particular, we focus on probabilistic graphical models (PGMs) and present the parallelisation of a collection of algorithms that deal with inference and learning of PGMs from data. Namely, maximum likelihood estimation, importance sampling, and greedy search for solving combinatorial optimisation problems. Through these concrete examples, we tackle the problem of defining efficient data structures for PGMs and parallel processing of same-size batches of data sets using Java 8 features. We also provide straightforward techniques to code parallel algorithms that seamlessly exploit multi-core processors. The experimental analysis, carried out using our open source AMIDST (Analysis of MassIve Data STreams) Java toolbox, shows the merits of the proposed solutions.Comment: Pre-print version of the paper presented in the special issue on Computational Intelligence Software at IEEE Computational Intelligence Magazine journa

Grouping complex systems for classification and parallel simulation

This thesis is concerned with grouping complex systems by means of concurrent model, in order to aid in (i) formulation of classifications and (ii) induction of parallel simulation programs. It observes, and seeks f~ furmalize _ and then exploit, the strong structural resemblance between complex systems and occam programs. The thesis hypothesizes that groups of complex systems may be discriminated according to shared structural and behavioural characteristics. Such an analysis of the complex systems domain may be performed in the abstract with the aid of a model for capturing interesting features of complex systems. The resulting groups would form a classification of complex systems. An additional hypothesis is that, insofar as the model is able to capture sufficient . programmatic information, these groups may be used to define, automatically, algorithmic skeletons for the concurrent simulation of complex systems. In order to test these hypotheses, a specification model and an accompanying formal notation are developed. The model expresses properties of complex systems in a mixture of object-oriented and process-oriented styles .. The model is then used as the basis for performing both classification and automatic induction of parallel simulation programs. The thesis takes the view that specification models should not be overly complex, especially if the specifications are meant to be executable. Therefore the requirement for explicit consideration of concurrency on the part of specifiers is minimized. The thesis formulates specifications of classes of cellular automata and neural networks according to the proposed model. Procedures for verificati6If - and induction of parallel simulation programs are also included