9858437
Heuristic Confidence Intervals for the Gamma Test
The Gamma test is a fast non-parametric algorithm for estimating the noise variance in an input/output dataset modulo the best smooth model. In this short note we communicate a heuristic method for computing confidence intervals for the noise estimate returned by a Gamma test analysis.
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.
14418558
Adaptive neuro-genetic control of chaos applied to the attitude control problem
Conventional adaptive control techniques have, for the most part, been based on methods for linear or weakly non-linear systems. More recently, neural network and genetic algorithm controllers have started to be applied to complex, non-linear dynamic systems. The control of chaotic dynamic systems poses a series of especially challenging problems. In this paper, an adaptive control architecture using neural networks and genetic algorithms is applied to a complex, highly nonlinear, chaotic dynamic system: the adaptive attitude control problem (for a satellite), in the presence of large, external forces (which left to themselves led the system into a chaotic motion). In contrast to the OGY method, which uses small control adjustments to stabilize a chaotic system in an otherwise unstable but natural periodic orbit of the system, the neuro-genetic controller may use large control adjustments and proves capable of effectively attaining any specified system state, with no a prioriknowledge of the dynamics, even in the presence of significant noise.
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.
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
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.
2433067
Egocentric depth judgments in optical, see-through augmented reality
A fundamental problem in optical, see-through augmented reality (AR) is characterizing how it affects the perception of spatial layout and depth. This problem is important because AR system developers need to both place graphics in arbitrary spatial relationships with real-world objects, and to know that users will perceive them in the same relationships. Furthermore, AR makes possible enhanced perceptual techniques that have no real-world equivalent, such as x-ray vision, where AR users are supposed to perceive graphics as being located behind opaque surfaces. This paper reviews and discusses protocols for measuring egocentric depth judgments in both virtual and augmented environments, and discusses the well-known problem of depth underestimation in virtual environments. It then describes two experiments that measured egocentric depth judgments in AR. Experiment I used a perceptual matching protocol to measure AR depth judgments at medium and far-field distances of 5 to 45 meters. The experiment studied the effects of upper versus lower visual field location, the x-ray vision condition, and practice on the task. The experimental findings include evidence for a switch in bias, from underestimating to overestimating the distance of AR-presented graphics, at ~ 23 meters, as well as a quantification of how much more difficult the x-ray vision condition makes the task. Experiment II used blind walking and verbal report protocols to measure AR depth judgments at distances of 3 to 7 meters. The experiment examined real-world objects, real-world objects seen through the AR display, virtual objects, and combined real and virtual objects. The results give evidence that the egocentric depth of AR objects is underestimated at these distances, but to a lesser degree than has previously been found for most virtual reality environments. The results are consistent with previous studies that have implicated a restricted field-of-view, combined with an inability for observers to scan the ground plane in a near-to-far direction, as explanations for the observed depth underestimation.
33714344
Editorial note
20473212
Towards a formal theory of defeasible deontic conditionals
The paper outlines the approach to the analysis of deontic conditionals taken in earlier work of Jones and Pörn, comparing it briefly with two main trends within dyadic deontic logic, and discussing problems associated with the augmentation principle and the factual detachment principle. A modification of the Jones and Pörn system is then presented, using a classical but not normal (in the sense of Chellas) deontic modality, to provide the basis for an alternative analysis of deontic conditionals. This new analysis validates neither the factual detachment nor the augmentation principles. However, influenced by the approach of Delgrande to default reasoning, it is shown how a restricted form of factual detachment might be accommodated within the revised system.
4157117
Data Access and Integration in the ISPIDER Proteomics Grid
Grid computing has great potential for supporting the integration of complex, fast changing biological data repositories to enable distributed data analysis. One scenario where Grid computing has such potential is provided by proteomics resources which are rapidly being developed with the emergence of affordable, reliable methods to study the proteome. The protein identifications arising from these methods derive from multiple repositories which need to be integrated to enable uniform access to them. A number of technologies exist which enable these resources to be accessed in a Grid environment, but the independent development of these resources means that significant data integration challenges, such as heterogeneity and schema evolution, have to be met. This paper presents an architecture which supports the combined use of Grid data access (OGSA-DAI), Grid distributed querying (OGSA-DQP) and data integration (AutoMed) software tools to support distributed data analysis. We discuss the application of this architecture for the integration of several autonomous proteomics data resources.