Christopher Watkins

• Lecturer at Royal Holloway University of London

This work presents a population genetic model of evolution, which includes haploid selection, mutation, recombination, and drift. The mutation-selection equilibrium can be expressed exactly in closed form for arbitrary fitness functions without resorting to diffusion approximations. Tractability is achieved by generating new offspring using n-paren...

This work presents a population genetic model of evolution, which includes haploid selection, mutation, recombination, and drift. The mutation-selection equilibrium can be expressed exactly in closed form for arbitrary fitness functions without resorting to diffusion approximations. Tractability is achieved by generating new offspring using n-paren...

The evolution and function of imitation in animal learning has always been associated with its crucial role in cultural transmission and cultural evolution. Can imitation evolve in the absence of cultural transmission? We investigate a model in which imitation is unbundled from cultural transmission: an organism's adult phenotype is plastically alt...

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones‐based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and pote...

Contact tracing is widely considered as an effective procedure in the fight against epidemic diseases. However, one of the challenges for technology based contact tracing is the high number of false positives, questioning its trust-worthiness and efficiency amongst the wider population for mass adoption. To this end, this paper proposes a novel, ye...

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and pote...

Contact tracing is widely considered as an effective procedure in the fight against epidemic diseases. However, one of the challenges for technology based contact tracing is the high number of false positives, questioning its trust-worthiness and efficiency amongst the wider population for mass adoption. To this end, this paper proposes a novel, ye...

Here we introduce a data staging algorithm designed to reconstruct multiple time series databases into a partitioned and regularised database. The Data Aggregation Partition Reduction Algorithm, or DAPRA for short, was designed to solve the practical issue of effective and meaningful visualisation of irregularly sampled time series data. This paper...

Passengers travelling on the London underground tubes currently have no means of knowing their whereabouts between stations. The challenge for providing such service is that the London underground tunnels have no GPS, Wi-Fi, Bluetooth, or any kind of terrestrial signals to leverage. This paper presents a novel yet practical idea to track passengers...

We demonstrate a breach in smartphone location privacy through the accelerometer and magnetometer's footprints. The merits or otherwise of explicitly permissioned location sensors are not the point of this paper. Instead, our proposition is that other non-location-sensitive sensors can track users accurately when the users are in motion, as in trav...

The public transports provide an ideal means to enable contagious diseases transmission. This paper introduces a novel idea to detect co-location of people in such environment using just the ubiquitous geomagnetic field sensor on the smart phone. Essentially, given that all passengers must share the same journey between at least two consecutive sta...

An epidemic may be controlled or predicted if we can monitor the history of physical human contacts. As most people have a smart phone, a contact between two persons can be regarded as a handshake between the two phones. Our task becomes how to detect the moment the two mobile phones are close. In this paper, we investigate the possibility of using...

We show that evolutionary computation can be implemented as standard Markov-chain Monte-Carlo (MCMC) sampling. With some care, `genetic algorithms' can be constructed that are reversible Markov chains that satisfy detailed balance; it follows that the stationary distribution of populations is a Gibbs distribution in a simple factorised form. For so...

The complexity of gene expression data generated from microarrays and high-throughput sequencing make their analysis challenging. One goal of these analyses is to define sets of co-regulated genes and identify patterns of gene expression. To date, however, there is a lack of easily implemented methods that allow an investigator to visualize and int...

Abstract-Selective breeding is considered as a communication channel, in a novel way. The Shannon informational capacity of this channel is an upper limit on the amount of information that can be put into the genome by selection: this is a meaningful upper limit to the adaptive complexity of evolved organisms. We calculate the maximum adaptive comp...

Reinforcement learning is one of the means by which animals and artificial systems can learn to optimize their behaviour in the face of rewards and punishments. Reinforcement learning algorithms have been developed that are closely related to methods of dynamic programming, which is a general approach to optimal control. Reinforcement learning phen...

We propose a novel approach for categorizing text documents based on the use of a special kernel. The kernel is an inner product in the feature space generated by all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguously. The subsequences are weighted by an exponent...

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to...

We introduce a novel kernel for comparing two text documents. The kernel is an inner product in the feature space consisting of all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguously. The subsequences are weighted by an exponentially decaying factor of their full...

> (x); (1.1) 1. `(x) = ( 1; x ? 0 0; otherwise Generic author design sample pages 1999/07/12 15:50 2 Support Vector Density Estimation where instead of knowing the distribution function F (x) we are given the iid (independently and identically distributed) data x 1 ; : : : ; x ` (1.2) generated by F . The problem of density estimation is known to b...

Support Vector Machines using ANOVA Decomposition Kernels (SVAD) [Vapng] are a way of imposing a structure on multi-dimensional kernels which are generated as the tensor product of one-dimensional kernels. This gives more accurate control over the capacity of the learning machine (VCdimension) . SVAD uses ideas from ANOVA decomposition methods and...

this report we describe how the Support Vector (SV) technique of solving linear operator equations can be applied to the problem of density estimation [4]. We present a new optimization procedure and set of kernels closely related to current SV techniques that guarantee the monotonicity of the approximation. This technique estimates densities with...

this paper. Thanks also to M. Stitson for writing the code for one-against-one and one-against-all SV classification. We also thank Kai Vogtlaender for useful comments. In communication with V. Vapnik and V. Blanz we discovered they independently derived the quadratic multi-class support vector method described in this report.

. The solution of binary classification problems using support vector machines (SVMs) is well developed, but multi-class problems with more than two classes have typically been solved by combining independently produced binary classifiers. We propose a formulation of the SVM that enables a multi-class pattern recognition problem to be solved in a s...

The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university...

The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university...

A typical problem in portfolio selection in stock markets is that it is not clear which of the many available strategies should be used. We apply a general algorithm of prediction with expert advice (the Aggregating Algorithm) to two different idealizations of the stock mar- ket. One is the well-known game introduced by Cover in connection with his...

this report we describe how the Support Vector (SV) technique of solving linear operator equations can be applied to the problem of density estimation [4]. We present a new optimization procedure and set of kernels closely related to current SV techniques that guarantee the monotonicity of the approximation. This technique estimates densities with...

Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. This paper present...

Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. This paper present...

Many of the artificial neural network models so far proposed `learn' nonlinear functional mappings from training examples. For example, the multilayer perceptron of D.E. Rumelhart and J.L. McClelland (1984) and the CMAC of J.A. Albus (1981) are both devices of this type. Neural networks are not the only function approximation methods available, and...

In this report we show how the class of adaptive prediction methods that Sutton called "temporal difference," or TD, methods are related to the theory of squential decision making. TD methods have been used as "adaptive critics" in connectionist learning systems, and have been proposed as models of animal learning in classical conditioning experime...

Photocopy. Supplied by British Library. Thesis (Ph. D.)--King's College, Cambridge, 1989.