How training and testing histories affect generalization: a test of simple neural networks

We show that a simple network model of associative learning can\ud reproduce three findings that arise from particular training and\ud testing procedures in generalization experiments: the effect of 1)\ud ``errorless learning'' and 2) extinction testing on peak shift, and\ud 3) the central tendency effect. These findings provide a true test\ud of the network model, which was developed to account for other\ud penhomena, and highlight the potential of neural networks to study\ud phenomena that depend on sequences of experiences with many stimuli.\ud Our results suggest that at least some such phenomena, e.g.,\ud stimulus range effects, may derive from basic mechanisms of\ud associative memory rather than from more complex memory processes

The Bulge-Halo Connection in Galaxies: A Physical Interpretation of the Vcirc-sigma_0 Relation

We explore the dependence of the ratio of a galaxy's circular velocity, Vcirc, to its central velocity dispersion, sigma_0, on morphology, or equivalently total light concentration. Such a dependence is expected if light traces the mass. Over the full range of galaxy types, masses and brightnesses, and assuming that the gas velocity traces the circular velocity, we find that galaxies obey the relation log(Vcirc/sigma_0)= 0.63-0.11*C28 where C28=5log(r80/r20) and the radii are measured at 80 percent and 20 percent of the total light. Massive galaxies scatter about the Vcirc = sqrt(2)*sigma_0 line for isothermal stellar systems. Disk galaxies follow the simple relation Vcirc/sigma_0=2(1-B/T), where B/T is the bulge-to-total light ratio. For pure disks, C28~2.8, B/T -> 0, and Vcirc~=2*sigma_0. Self-consistent equilibrium galaxy models from Widrow & Dubinski (2005) constrained to match the size-luminosity and velocity-luminosity relations of disk galaxies fail to match the observed Vcirc/sigma_0 distribution. Furthermore, the matching of dynamical models for Vcirc(r)/sigma(r) with observations of dwarf and elliptical galaxies suffers from limited radial coverage and relatively large error bars; for dwarf systems, however, kinematical measurements at the galaxy center and optical edge suggest Vcirc(Rmax) > 2*sigma_0 (in contrast with past assumptions that Vcirc = sqrt(2)*sigma_0 for dwarfs.) The Vcirc-sigma_0-C28 relation has direct implications for galaxy formation and dynamical models, galaxy scaling relations, the mass function of galaxies, and the links between respective formation and evolution processes for a galaxy's central massive object, bulge, and dark matter halo.Comment: Accepted for publication in ApJL. Current version matches ApJL page requiremen

Casimir Effect in Background of Static Domain Wall

In this paper we investigate the vacuum expectation values of energy- momentum tensor for conformally coupled scalar field in the standard parallel plate geometry with Dirichlet boundary conditions and on background of planar domain wall case. First we calculate the vacuum expectation values of energy-momentum tensor by using the mode sums, then we show that corresponding properties can be obtained by using the conformal properties of the problem. The vacuum expectation values of energy-momentum tensor contains two terms which come from the boundary conditions and the the gravitational background. In the Minkovskian limit our results agree with those obtained in [3].Comment: 8 Page

String cosmological model in the presence of a magnetic flux

A Bianchi type I string cosmological model in the presence of a magnetic flux is investigated. A few plausible assumptions regarding the parametrization of the cosmic string and magneto-fluid are introduced and some exact analytical solutions are presented.Comment: 9 pages, 4 Figure

Towards hardware acceleration of neuroevolution for multimedia processing applications on mobile devices

This paper addresses the problem of accelerating large artificial neural networks (ANN), whose topology and weights can evolve via the use of a genetic algorithm. The proposed digital hardware architecture is capable of processing any evolved network topology, whilst at the same time providing a good trade off between throughput, area and power consumption. The latter is vital for a longer battery life on mobile devices. The architecture uses multiple parallel arithmetic units in each processing element (PE). Memory partitioning and data caching are used to minimise the effects of PE pipeline stalling. A first order minimax polynomial approximation scheme, tuned via a genetic algorithm, is used for the activation function generator. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design

Cascading on extragalactic background light

High-energy gamma-rays propagating in the intergalactic medium can interact with background infrared photons to produce e+e- pairs, resulting in the absorption of the intrinsic gamma-ray spectrum. TeV observations of the distant blazar 1ES 1101-232 were thus recently used to put an upper limit on the infrared extragalactic background light density. The created pairs can upscatter background photons to high energies, which in turn may pair produce, thereby initiating a cascade. The pairs diffuse on the extragalactic magnetic field (EMF) and cascade emission has been suggested as a means for measuring its intensity. Limits on the IR background and EMF are reconsidered taking into account cascade emissions. The cascade equations are solved numerically. Assuming a power-law intrinsic spectrum, the observed 100 MeV - 100 TeV spectrum is found as a function of the intrinsic spectral index and the intensity of the EMF. Cascades emit mainly at or below 100 GeV. The observed TeV spectrum appears softer than for pure absorption when cascade emission is taken into account. The upper limit on the IR photon background is found to be robust. Inversely, the intrinsic spectra needed to fit the TeV data are uncomfortably hard when cascade emission makes a significant contribution to the observed spectrum. An EMF intensity around 1e-8 nG leads to a characteristic spectral hump in the GLAST band. Higher EMF intensities divert the pairs away from the line-of-sight and the cascade contribution to the spectrum becomes negligible.Comment: 5 pages, to be published as a research note in A&

An efficient hardware architecture for a neural network activation function generator

This paper proposes an efficient hardware architecture for a function generator suitable for an artificial neural network (ANN). A spline-based approximation function is designed that provides a good trade-off between accuracy and silicon area, whilst also being inherently scalable and adaptable for numerous activation functions. This has been achieved by using a minimax polynomial and through optimal placement of the approximating polynomials based on the results of a genetic algorithm. The approximation error of the proposed method compares favourably to all related research in this field. Efficient hardware multiplication circuitry is used in the implementation, which reduces the area overhead and increases the throughput

Empowering and assisting natural human mobility: The simbiosis walker

This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Implicitly Constrained Semi-Supervised Least Squares Classification

We introduce a novel semi-supervised version of the least squares classifier. This implicitly constrained least squares (ICLS) classifier minimizes the squared loss on the labeled data among the set of parameters implied by all possible labelings of the unlabeled data. Unlike other discriminative semi-supervised methods, our approach does not introduce explicit additional assumptions into the objective function, but leverages implicit assumptions already present in the choice of the supervised least squares classifier. We show this approach can be formulated as a quadratic programming problem and its solution can be found using a simple gradient descent procedure. We prove that, in a certain way, our method never leads to performance worse than the supervised classifier. Experimental results corroborate this theoretical result in the multidimensional case on benchmark datasets, also in terms of the error rate.Comment: 12 pages, 2 figures, 1 table. The Fourteenth International Symposium on Intelligent Data Analysis (2015), Saint-Etienne, Franc