On the Relationship Between the Generalized Equality Classifier and ART 2 Neural Networks

In this paper, we introduce the Generalized Equality Classifier (GEC) for use as an unsupervised clustering algorithm in categorizing analog data. GEC is based on a formal definition of inexact equality originally developed for voting in fault tolerant software applications. GEC is defined using a metric space framework. The only parameter in GEC is a scalar threshold which defines the approximate equality of two patterns. Here, we compare the characteristics of GEC to the ART2-A algorithm (Carpenter, Grossberg, and Rosen, 1991). In particular, we show that GEC with the Hamming distance performs the same optimization as ART2. Moreover, GEC has lower computational requirements than AR12 on serial machines

On the Relationship Between the Generalized Equality Classifier and ART 2 Neural Networks

In this paper, we introduce the Generalized Equality Classifier (GEC) for use as an unsupervised clustering algorithm in categorizing analog data. GEC is based on a formal definition of inexact equality originally developed for voting in fault tolerant software applications. GEC is defined using a metric space framework. The only parameter in GEC is a scalar threshold which defines the approximate equality of two patterns. Here, we compare the characteristics of GEC to the ART2-A algorithm (Carpenter, Grossberg, and Rosen, 1991). In particular, we show that GEC with the Hamming distance performs the same optimization as ART2. Moreover, GEC has lower computational requirements than AR12 on serial machines

WALS estimation and forecasting in factor-based dynamic models with an application to Armenia

Two model averaging approaches are used and compared in estimating and forecasting dynamic factor models, the well-known BMA and the recently developed WALS. Both methods propose to combine frequentist estimators using Bayesian weights. We apply our framework to the Armenian economy using quarterly data from 2000–2010, and we estimate and forecast real GDP and inflation dynamics.Dynamic models;Factor analysis;Model averaging;Monte Carlo;Armenia

Eliminating Redundant Training Data Using Unsupervised Clustering Techniques

Training data for supervised learning neural networks can be clustered such that the input/output pairs in each cluster are redundant. Redundant training data can adversely affect training time. In this paper we apply two clustering algorithms, ART2 -A and the Generalized Equality Classifier, to identify training data clusters and thus reduce the training data and training time. The approach is demonstrated for a high dimensional nonlinear continuous time mapping. The demonstration shows six-fold decrease in training time at little or no loss of accuracy in the handling of evaluation data

In vivo contact stresses at the radiocarpal joint using a finite element method of the complete wrist joint

A small number of cadaveric studies have been carried out looking at the force transmission through the radiocarpal joint. In this study subject specific finite element models were created of the whole wrist joint using measured biomechanical data to capture the forces acting on the wrist with the hand generating a maximum gripping force

On the Choice of Prior in Bayesian Model Averaging

Bayesian model averaging attempts to combine parameter estimation and model uncertainty in one coherent framework. The choice of prior is then critical. Within an explicit framework of ignorance we define a ‘suitable’ prior as one which leads to a continuous and suitable analog to the pretest estimator. The normal prior, used in standard Bayesian model averaging, is shown to be unsuitable. The Laplace (or lasso) prior is almost suitable. A suitable prior (the Subbotin prior) is proposed and its properties are investigated.Model averaging;Bayesian analysis;Subbotin prior

Warm water deuterium fractionation in IRAS 16293-2422 - The high-resolution ALMA and SMA view

Measuring the water deuterium fractionation in the inner warm regions of low-mass protostars has so far been hampered by poor angular resolution obtainable with single-dish ground- and space-based telescopes. Observations of water isotopologues using (sub)millimeter wavelength interferometers have the potential to shed light on this matter. Observations toward IRAS 16293-2422 of the 5(3,2)-4(4,1) transition of H2-18O at 692.07914 GHz from Atacama Large Millimeter/submillimeter Array (ALMA) as well as the 3(1,3)-2(2,0) of H2-18O at 203.40752 GHz and the 3(1,2)-2(2,1) transition of HDO at 225.89672 GHz from the Submillimeter Array (SMA) are presented. The 692 GHz H2-18O line is seen toward both components of the binary protostar. Toward one of the components, "source B", the line is seen in absorption toward the continuum, slightly red-shifted from the systemic velocity, whereas emission is seen off-source at the systemic velocity. Toward the other component, "source A", the two HDO and H2-18O lines are detected as well with the SMA. From the H2-18O transitions the excitation temperature is estimated at 124 +/- 12 K. The calculated HDO/H2O ratio is (9.2 +/- 2.6)*10^(-4) - significantly lower than previous estimates in the warm gas close to the source. It is also lower by a factor of ~5 than the ratio deduced in the outer envelope. Our observations reveal the physical and chemical structure of water vapor close to the protostars on solar-system scales. The red-shifted absorption detected toward source B is indicative of infall. The excitation temperature is consistent with the picture of water ice evaporation close to the protostar. The low HDO/H2O ratio deduced here suggests that the differences between the inner regions of the protostars and the Earth's oceans and comets are smaller than previously thought.Comment: Accepted for publication in Astronomy & Astrophysic

Asymptotic properties of the solutions of a differential equation appearing in QCD

We establish the asymptotic behaviour of the ratio $h^\prime(0)/h(0)$ for $\lambda\rightarrow\infty$, where $h(r)$ is a solution, vanishing at infinity, of the differential equation $h^{\prime\prime}(r) = i\lambda \omega (r) h(r)$ on the domain $0 \leq r <\infty$ and $\omega (r) = (1-\sqrt{r} K_1(\sqrt{r}))/r$. Some results are valid for more general $\omega$'s.Comment: 6 pages, late

Aviation Law Comes Home to the Main Street Lawyer

Well controlled in length and highly aligned ZnO nanorods were grown on the gold-coated glass substrate by hydrothermal growth method. ZnO nanorods were functionalised with selective thallium (I) ion ionophore dibenzyldiaza-18-crown-6 (DBzDA18C6). The thallium ion sensor showed wide linear potentiometric response to thallium (I) ion concentrations ( M to  M) with high sensitivity of 36.87 ± 1.49 mV/decade. Moreover, thallium (I) ion demonstrated fast response time of less than 5 s, high selectivity, reproducibility, storage stability, and negligible response to common interferents. The proposed thallium (I) ion-sensor electrode was also used as an indicator electrode in the potentiometric titration, and it has shown good stoichiometric response for the determination of thallium (I) ion