Fluctuation Spectra and Force Generation in Non-equilibrium Systems

Many biological systems are appropriately viewed as passive inclusions immersed in an active bath: from proteins on active membranes to microscopic swimmers confined by boundaries. The non-equilibrium forces exerted by the active bath on the inclusions or boundaries often regulate function, and such forces may also be exploited in artificial active materials. Nonetheless, the general phenomenology of these active forces remains elusive. We show that the fluctuation spectrum of the active medium, the partitioning of energy as a function of wavenumber, controls the phenomenology of force generation. We find that for a narrow, unimodal spectrum, the force exerted by a non-equilibrium system on two embedded walls depends on the width and the position of the peak in the fluctuation spectrum, and oscillates between repulsion and attraction as a function of wall separation. We examine two apparently disparate examples: the Maritime Casimir effect and recent simulations of active Brownian particles. A key implication of our work is that important non-equilibrium interactions are encoded within the fluctuation spectrum. In this sense the noise becomes the signal

Continuous Reinforced Snap-Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks

A new continuous learning method is used to optimise the selection of services in response to user requests in an active computer network simulation environment. The learning is an enhanced version of the ‘snap-drift’ algorithm, which employs the complementary concepts of fast, minimalist (snap) learning and slower drift (towards the input patterns) learning, in a non-stationary environment where new patterns arrive continually. Snap is based on Adaptive Resonance Theory, and drift on Learning Vector Quantisation. The new algorithm swaps its learning style between these two self-organisational modes when declining performance is detected, but maintains the same learning mode during episodes of improved performance. Performance updates occur at the end of each epoch. Reinforcement is implemented by enabling learning on any given pattern with a probability that increases linearly with declining performance. This method, which is capable of rapid re-learning, is used in the design of a modular neural network system: Performance-guided Adaptive Resonance Theory (P-ART). Simulations involving a requirement to continuously adapt to make appropirate decisions within a BT active computer network environment, demonstrate the learning is stable, and able to discover alternative solutions in rapid response to new performance requirements or significant changes in the stream of input patterns

Modal Learning in a Neural Network

This paper presents an application of the snap-drift modal learning algorithm developed in recent years by Lee and Palmer-Brown (Lee, 2004a). The application involves phrase recognition using a set of phrases from the Lancaster Parsed Corpus (LPC) (Garside, 1987). The learning algorithm is the classifier version of snap-drift. The twin modes of minimalist learning (snap) and slow drift towards the input pattern are applied alternately. Each neuron of the Snap-Drift Neural Network (SDNN) swaps between snap and drift modes when declining performance is indicated on that particular node, so that each node has its learning mode toggled independently of the other nodes. Learning on each node is also reinforced by enabling learning with a probability that decreases with increasing performance. The simulations demonstrate that learning is stable, and the results have consistently shown similar classification performance and advantages in terms of speed in comparison with a Multilayer Perceptron (MLP) and back-propagation neural networks applied to the same problem

The Analysis of Network Manager’s Behaviour using a Self-Organising Neural Networks

We present a novel neural network method for the analysis and interpretation of data that describes user interaction with a training tool. The method is applied to the interaction between trainee network managers and a simulated network management system. A simulation based approach to the task of efficiently training network managers, through the use of a simulated network, was originally presented by Pattinson [2000]. The motivation was to provide a tool for exposing trainee network managers to a life like situation, where both normal network operation and ‘fault’ scenarios could be simulated in order to train the network manager. The data logged by this system describes the detailed interaction between trainee network manager and simulated network. The work presented here provides an analysis of this interaction data that enables an assessment of the capabilities of the network manager as well as an understanding of how the network management tasks are being approached. A neural network architecture [Lee, Palmer-Brown, Roadknight 2004] is adapted and implemented in order to perform an exploratory data analysis of the interaction data. The neural network architecture employs a novel form of continuous self-organisation to discover key features, and thus provide new insights into the data

Phonetic Feature Discovery in Speech using Snap-Drift

This paper presents a new application of the snapdrift algorithm [1]: feature discovery and clustering of speech waveforms from nonstammering and stammering speakers. The learning algorithm is an unsupervised version of snapdrift which employs the complementary concepts of fast, minimalist learning (snap) & slow drift (towards the input pattern) learning. The SnapDrift Neural Network (SDNN) is toggled between snap and drift modes on successive epochs. The speech waveforms are drawn from a phonetically annotated corpus, which facilitates phonetic interpretation of the classes of patterns discovered by the SDNN

Diagnostic Feedback by Snap-drift Question Response Grouping

This work develops a method for incorporation into an on-line system to provide carefully targeted guidance and feedback to students. The student answers on-line multiple choice questions on a selected topic, and their responses are sent to a Snap-Drift neural network trained with responses from a past students. Snap-drift is able to categorise the learner's responses as having a significant level of similarity with a subset of the students it has previously categorised. Each category is associated with feedback composed by the lecturer on the basis of the level of understanding and prevalent misconceptions of that category-group of students. In this way the feedback addresses the level of knowledge of the individual and guides them towards a greater understanding of particular concepts. The feedback is concept-based rather than tied to any particular question, and so the learner is encouraged to retake the same test and receives different feedback depending on their evolving state of knowledge

Snap-Drift Neural Network for Selecting Student Feedback

This paper investigates the application of the snap- drift neural network (SDNN) to the provision of guided student learning in formative assessments. SDNN is able to adapt rapidly by performing a combination of fast, convergent, minimal intersection learning (snap) and Learning Vector Quantization (drift) to capture both precise sub-features in the data and more general holistic features. Snap and drift are combined within a modal learning system that toggles its learning style between the two modes. In this particular application the SDNN is trained with responses from past students to Multiple Choice Questions (MCQs). The neural network is able to categorise the learner's responses as having a significant level of similarity with a subset of the students it has previously categorised. Each category is associated with feedback composed by the lecturer on the basis of the level of understanding and prevalent misconceptions of that category-group of students. The feedback addresses the level of knowledge of the individual and guides them towards a greater understanding of particular concepts. The trained snap-drift neural network is integrated into an on-line Multiple Choice Questions (MCQs) system. This approach has been implemented and trialled with two cohorts of students using data sets of student answers related to a topic from an Introduction to Computer System module. Results indicate that significant learning support is provided for the students