Global adaptation in networks of selfish components: emergent associative memory at the system scale

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning

Attitude determination of the spin-stabilized Project Scanner spacecraft

Attitude determination of spin-stabilized spacecraft using star mapping techniqu

Metallic phase of the quantum Hall effect in four-dimensional space

We study the phase diagram of the quantum Hall effect in four-dimensional (4D) space. Unlike in 2D, in 4D there exists a metallic as well as an insulating phase, depending on the disorder strength. The critical exponent $\nu\approx 1.2$ of the diverging localization length at the quantum Hall insulator-to-metal transition differs from the semiclassical value $\nu=1$ of 4D Anderson transitions in the presence of time-reversal symmetry. Our numerical analysis is based on a mapping of the 4D Hamiltonian onto a 1D dynamical system, providing a route towards the experimental realization of the 4D quantum Hall effect.Comment: 4+epsilon pages, 3 figure

Bio-linguistic transition and Baldwin effect in an evolutionary naming-game model

We examine an evolutionary naming-game model where communicating agents are equipped with an evolutionarily selected learning ability. Such a coupling of biological and linguistic ingredients results in an abrupt transition: upon a small change of a model control parameter a poorly communicating group of linguistically unskilled agents transforms into almost perfectly communicating group with large learning abilities. When learning ability is kept fixed, the transition appears to be continuous. Genetic imprinting of the learning abilities proceeds via Baldwin effect: initially unskilled communicating agents learn a language and that creates a niche in which there is an evolutionary pressure for the increase of learning ability.Our model suggests that when linguistic (or cultural) processes became intensive enough, a transition took place where both linguistic performance and biological endowment of our species experienced an abrupt change that perhaps triggered the rapid expansion of human civilization.Comment: 7 pages, minor changes, accepted in Int.J.Mod.Phys.C, proceedings of Max Born Symp. Wroclaw (Poland), Sept. 2007. Java applet is available at http://spin.amu.edu.pl/~lipowski/biolin.html or http://www.amu.edu.pl/~lipowski/biolin.htm

Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e.g. from short-axis MR images of the left-ventricle. In this work we propose a recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units. RFCN combines anatomical detection and segmentation into a single architecture that is trained end-to-end thus significantly reducing computational time, simplifying the segmentation pipeline, and potentially enabling real-time applications. We report on an investigation of RFCN using two datasets, including the publicly available MICCAI 2009 Challenge dataset. Comparisons have been carried out between fully convolutional networks and deep restricted Boltzmann machines, including a recurrent version that leverages inter-slice spatial correlation. Our studies suggest that RFCN produces state-of-the-art results and can substantially improve the delineation of contours near the apex of the heart.Comment: MICCAI Workshop RAMBO 201

Cavity method for quantum spin glasses on the Bethe lattice

We propose a generalization of the cavity method to quantum spin glasses on fixed connectivity lattices. Our work is motivated by the recent refinements of the classical technique and its potential application to quantum computational problems. We numerically solve for the phase structure of a connectivity $q=3$ transverse field Ising model on a Bethe lattice with $\pm J$ couplings, and investigate the distribution of various classical and quantum observables.Comment: 27 pages, 9 figure

Enzyme and Tissue Alterations in Fishes: A Measure of Water Quality

A variety of freshwater fishes were studied by light and electron microscopy, enzyme histochemical and biochemical methods, The objective was to determine normal structure and function in specific target organs and to compare these to altered states in aquatic pollution. The basic question, can fish tissues and enzymes serve as indicators of water quality?, was asked. Microscopic alteration in gill was indicative of copper toxicity at an exposure of 20 parts per billion, Gross and light microscopic alterations were indicative of a single exposure of channel catfish to 15 parts per million of methyl mercuric chloride (CH3HgCl). Microscopic and correlated biochemical study fingerprinted the alterations in cells at an exposure of 0.67 parts per million CH3HgC1. The developments of pathobiological autopsy techniques for the assessment of water quality is discussed

Using generative models for handwritten digit recognition

We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B-splines with Gaussian ``ink generators'' spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of pre-normalization of input images, but can handle arbitrary scalings, translations and a limited degree of image rotation. We have demonstrated our method of fitting models to images does not get trapped in poor local minima. The main disadvantage of the method is it requires much more computation than more standard OCR techniques

The background from single electromagnetic subcascades for a stereo system of air Cherenkov telescopes

The MAGIC experiment, a very large Imaging Air Cherenkov Telescope (IACT) with sensitivity to low energy (E < 100 GeV) VHE gamma rays, has been operated since 2004. It has been found that the gamma/hadron separation in IACTs becomes much more difficult below 100 GeV [Albert et al 2008] A system of two large telescopes may eventually be triggered by hadronic events containing Cherenkov light from only one electromagnetic subcascade or two gamma subcascades, which are products of the single pi^0 decay. This is a possible reason for the deterioration of the experiment's sensitivity below 100 GeV. In this paper a system of two MAGIC telescopes working in stereoscopic mode is studied using Monte Carlo simulations. The detected images have similar shapes to that of primary gamma-rays and they have small sizes (mainly below 400 photoelectrons (p.e.)) which correspond to an energy of primary gamma-rays below 100 GeV. The background from single or two electromagnetic subcascdes is concentrated at energies below 200 GeV. Finally the number of background events is compared to the number of VHE gamma-ray excess events from the Crab Nebula. The investigated background survives simple cuts for sizes below 250 p.e. and thus the experiment's sensitivity deteriorates at lower energies.Comment: 15 pages, 7 figures, published in Journ.of Phys.