14346877
The Construction of Smooth Models using Irregular Embeddings Determined by a Gamma Test Analysis
One of the key problems in forming a smooth model from input-output data is the determination of which input variables are relevant in predicting a given output. In this paper, we show how the Gamma test can be used to select that combination of input variables which can best be employed to form a smooth model of an output. For time series prediction this amounts to the selection of an appropriate irregular embedding. We give some simple zero noise examples of time series analysis, and illustrate how using these techniques a binary message encoded into a chaotic carrier can be retrieved without knowledge of the dynamics used to generate the carrier. Provided the underlying dynamics are such as to produce a smooth embedding model with bounded partial derivatives, the sampling distribution is dense in input space, and any associated distribution of measurement error has the first few moments bounded, so that the typical prerequisite conditions of the Gamma test are satisfied, we conclude that the Gamma test is an effective tool in the determination of irregular time series embeddings. These techniques can also be useful in practical applications which involve filtering seismic data to detect anomalous events.
11348191
A note on the Gamma test
This note describes a simple technique, the Gamma (or Near Neighbour) test, which in many cases can be used to considerably simplify the design process of constructing a smooth data model such as a neural network. The Gamma test is a data analysis routine, that (in an optimal implementation) runs in time O(MlogM)as M→∞,where Mis the number of sample data points, and which aims to estimate the best Mean Squared Error (MSError) that can be achieved by any continuous or smooth (bounded first partial derivatives) data model constructed using the data.
172513
Comparing networks with differing neural-node functions using transputer-based genetic algorithms
Different neural net node computational functions are compared using feedforward backpropagation networks. Three node types are examined: the standard model, ellipsoidal nodes and quadratic nodes. After preliminary experiments on simple small problems, in which quadratic nodes performed very well, networks of differing nodes types are applied to the speech recognition 104 speaker E-task using a fixed architecture. Ellipsoidal nodes were found to work well, but not as well as the standard model. Quadratic nodes did not perform well on the larger task. To facilitate an architecture independent comparison a transputer-based genetic algorithm is then used to compare ellipsoidal and mixed ellipsoidal and standard networks with the standard model. These experiments confirmed the earlier conclusion that ellipsoidal networks could not compare favourably with the standard model on the 104 speaker E-task. In an evolutionary search in which layer node types were free to adjust ellipsoidal nodes had a tendency to become extinct or barely survive. If the presence of ellipsoidal nodes was enforced then the resulting networks again performed poorly when compared with the standard model.
14169547
Non-linear modelling and chaotic neural networks
Proposes a simple methodology to construct an iterative neural network which mimics a given chaotic time series. The methodology uses the Gamma test to identify a suitable (possibly irregular) embedding of the chaotic time series from which a one step predictive model may be constructed. This model is then iterated to produce a close approximation to the original chaotic dynamics. Having constructed such networks we show how the chaotic dynamics may be stabilised using time-delayed feedback, which is a plausible method for stabilisation in biological neural systems. Using delayed feedback control, which is activated in the presence of a stimulus, such networks can behave as an associative memory, in which the act of recognition corresponds to stabilisation onto an unstable periodic orbit. We briefly illustrate how two identical dynamically independent copies of such a chaotic iterative network may be synchronised using the delayed feedback method. Although less biologically plausible, these techniques may have interesting applications in secure communications.
9554976
Evolutionary Divide and Conquer (I): A Novel Genetic Approach to the TSP
Experiments with genetic algorithms using permutation operators applied to the traveling salesman problem (TSP) tend to suggest that these algorithms fail in two respects when applied to very large problems: they scale rather poorly as the number of cities n increases, and the solution quality degrades rapidly. We propose an alternative approach for genetic algorithms applied to hard combinatoric search which we call Evolutionary Divide and Conquer (EDAC). This method has potential for any search problem in which knowledge of good solutions for subproblems can be exploited to improve the solution of the problem itself. The idea is to use the genetic algorithm to explore the space of problem subdivisions rather than the space of solutions themselves. We give some preliminary results of this method applied to the geometric TSP.
3022268
Feature selection for genetic sequence classification
MOTIVATION
Most of the existing methods for genetic sequence classification are based on a computer search for homologies in nucleotide or amino acid sequences. The standard sequence alignment programs scale very poorly as the number of sequences increases or the degree of sequence identity is <30%. Some new computationally inexpensive methods based on nucleotide or amino acid compositional analysis have been proposed, but prediction results are still unsatisfactory and depend on the features chosen to represent the sequences.


RESULTS
In this paper, a feature selection method based on the Gamma (or near-neighbour) test is proposed. If there is a continuous or smooth map from feature space to the classification target values, the Gamma test gives an estimate for the mean-squared error of the classification, despite the fact that one has no a priori knowledge of the smooth mapping. We can search a large space of possible feature combinations for a combination which gives a smallest estimated mean-squared error using a genetic algorithm. The method was used for feature selection and classification of the large subunits of rRNA according to RDP (Ribosomal Database Project) phylogenetic classes. The sequences were represented by dinucleotide frequency distribution. The nearest-neighbour criterion has been used to estimate the predictive accuracy of the classification based on the selected features. For examples discussed, we found that the classification according to the first nearest neighbour is correct for 80% of the test samples. If we consider the set of the 10 nearest neighbours, then 94% of the test samples are classified correctly.


AVAILABILITY
The principal novel component of this method is the Gamma test and this can be downloaded compiled for Unix Sun 4, Windows 95 and MS-DOS from http://www.cs.cf.ac.uk/ec/


CONTACT
s.margetts@cs.cf.ac.uk
9096556
A language extension for expressing constraints on data access
Controlled sharing of information is needed and desirable for many applications and is supported in operating systems by access control mechanisms. This paper shows how to extend programming languages to provide controlled sharing. The extension permits expression of access constraints on shared data. Access constraints can apply both to simple objects, and to objects that are components of larger objects, such as bank account records in a bank's data base. The constraints are stated declaratively, and can be enforced by static checking similar to type checking. The approach can be used to extend any strongly-typed language, but is particularly suitable for extending languages that support the notion of abstract data types.
5706514
Reducing power while increasing performance with supercisc
Multiprocessor Systems on Chips (MPSoCs) have become a popular architectural technique to increase performance. However, MPSoCs may lead to undesirable power consumption characteristics for computing systems that have strict power budgets, such as PDAs, mobile phones, and notebook computers. This paper presents the super-complex instruction-set computing (SuperCISC) Embedded Processor Architecture and, in particular, investigates performance and power consumption of this device compared to traditional processor architecture-based execution. SuperCISC is a heterogeneous, multicore processor architecture designed to exceed performance of traditional embedded processors while maintaining a reduced power budget compared to low-power embedded processors. At the heart of the SuperCISC processor is a multicore VLIW (Very Large Instruction Word) containing several homogeneous execution cores/functional units. In addition, complex and heterogeneous combinational hardware function cores are tightly integrated to the core VLIW engine providing an opportunity for improved performance and reduced energy consumption. Our SuperCISC processor core has been synthesized for both a 90-nm Stratix II Field Programmable Gate Aray (FPGA) and a 160-nm standard cell Application-Specific Integrated Circuit (ASIC) fabrication process from OKI, each operating at approximately 167 MHz for the VLIW core. We examine several reasons for speedup and power improvement through the SuperCISC architecture, including predicated control flow, cycle compression, and a reduction in arithmetic power consumption, which we call power compression. Finally, testing our SuperCISC processor with multimedia and signal-processing benchmarks, we show how the SuperCISC processor can provide performance improvements ranging from 7X to 160X with an average of 60X, while also providing orders of magnitude of power improvements for the computational kernels. The power improvements for our benchmark kernels range from just over 40X to over 400X, with an average savings exceeding 130X. By combining these power and performance improvements, our total energy improvements all exceed 1000X. As these savings are limited to the computational kernels of the applications, which often consume approximately 90% of the execution time, we expect our savings to approach the ideal application improvement of 10X.
33714344
Editorial note
47276
A multi-processing software infrastructure for robotic systems
Robotic systems and their software design are based on the same principles found in other computer applications. However, while many computer systems to date are deeply rooted in concepts such as multi-processing, multi-threads, modularity, etc., robotic systems are still limited by mono-processing, centralized designs. The paper addresses these limitations by proposing a new philosophy for robotic system software design - software infrastructure $that allows for the design of an efficient, modular, fault-tolerant, and distributed software architecture. Two examples of this infrastructure applied to mobile robot navigation are also discussed.
206840293
Formal Specification of Security Requirements using the Theory of Normative Positions
We use a number of the examples presented in [Ting 1990] to illustrate how the formal theory of normative positions may serve as a tool for clarifying, and making precise, the specification of security requirements, particularly in regard to access control. We describe the basic features of the theory of normative positions (which has its roots in the analytical theory of law), and of the modal logics (deontic and action logics) involved in its formulation. We then indicate three levels of software we have under development, which aim to turn the analytical procedures into a practical tool. Our concluding remarks relate our discussion of Ting's examples to some particular issues in the formal specification of computer systems.