PQCD Analysis of Parton-Hadron Duality

We propose an extraction of the running coupling constant of QCD in the infrared region from experimental data on deep inelastic inclusive scattering at Bjorken x -> 1. We first attempt a perturbative fit of the data that extends NLO PQCD evolution to large x values and final state invariant mass, W, in the resonance region. We include both target mass corrections and large x resummation effects. These effects are of order O(1/Q^2), and they improve the agreement with the Q^2 dependence of the data. Standard analyses require the presence of additional power corrections, or dynamical higher twists, to achieve a fully quantitative fit. Our analysis, however, is regulated by the value of the strong coupling in the infrared region that enters through large x resummation effects, and that can suppress, or absorb, higher twist effects. Large x data therefore indirectly provide a measurement of this quantity that can be compared to extractions from other observables.Comment: 10 pages, 3 figure

Forebody tangential blowing for control at high angles of attack

A feasibility study to determine if the use of tangential leading edge blowing over the forebody could produce effective and practical control of the F-18 HARV aircraft at high angles of attack was conducted. A simplified model of the F-18 configuration using a vortex-lattice model was developed to obtain a better understanding of basic aerodynamic coupling effects and the influence of forebody circulation on lifting surface behavior. The effect of tangential blowing was estimated using existing wind tunnel data on normal forebody blowing and analytical studies of tangential blowing over conical forebodies. Incorporation of forebody blowing into the flight control system was investigated by adding this additional yaw control and sideforce generating actuator into the existing F-18 HARV simulation model. A control law was synthesized using LQG design methods that would schedule blowing rates as a function of vehicle sideslip, angle of attack, and roll and yaw rates

Wavelet-based voice morphing

This paper presents a new multi-scale voice morphing algorithm. This algorithm enables a user to transform one person's speech pattern into another person's pattern with distinct characteristics, giving it a new identity, while preserving the original content. The voice morphing algorithm performs the morphing at different subbands by using the theory of wavelets and models the spectral conversion using the theory of Radial Basis Function Neural Networks. The results obtained on the TIMIT speech database demonstrate effective transformation of the speaker identity

Voice morphing using the generative topographic mapping

In this paper we address the problem of Voice Morphing. We attempt to transform the spectral characteristics of a source speaker's speech signal so that the listener would believe that the speech was uttered by a target speaker. The voice morphing system transforms the spectral envelope as represented by a Linear Prediction model. The transformation is achieved by codebook mapping using the Generative Topographic Mapping, a non-linear, latent variable, parametrically constrained, Gaussian Mixture Model

Covariance, Dynamics and Symmetries, and Hadron Form Factors

We summarise applications of Dyson-Schwinger equations to the theory and phenomenology of hadrons. Some exact results for pseudoscalar mesons are highlighted with details relating to the U_A(1) problem. We describe inferences from the gap equation relating to the radius of convergence for expansions of observables in the current-quark mass. We recapitulate upon studies of nucleon electromagnetic form factors, providing a comparison of the ln-weighted ratios of Pauli and Dirac form factors for the neutron and proton.Comment: 9 pages, 2 figures. Contribution to proceedings of Workshop on Exclusive Reactions at High Momentum Transfer, May 21-24, 2007, Jefferson Lab, Newport News, V

Nucleation of Spontaneous Vortices in Trapped Fermi Gases Undergoing a BCS-BEC Crossover

We study the spontaneous formation of vortices during the superfluid condensation in a trapped fermionic gas subjected to a rapid thermal quench via evaporative cooling. Our work is based on the numerical solution of the time dependent crossover Ginzburg-Landau equation coupled to the heat diffusion equation. We quantify the evolution of condensate density and vortex length as a function of a crossover phase parameter from BCS to BEC. The more interesting phenomena occur somewhat nearer to the BEC regime and should be experimentally observable; during the propagation of the cold front, the increase in condensate density leads to the formation of supercurrents towards the center of the condensate as well as possible condensate volume oscillations.Comment: 5 pages, 3 figure

On Similarities between Inference in Game Theory and Machine Learning

In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both fields, and as proof of the usefulness of this approach, we use recent developments in each field to make useful improvements to the other. More specifically, we consider the analogies between smooth best responses in fictitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard fictitious play) we derive a novel moderated fictitious play algorithm and show that it is more likely than standard fictitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean field variational learning algorithms. We first show that the standard update rule of mean field variational learning is analogous to a Cournot adjustment within game theory. By analogy with fictitious play, we then suggest an improved update rule, and show that this results in fictitious variational play, an improved mean field variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in fictitious play, namely dynamic fictitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution)

The dwarf low surface brightness population in different environments of the Local Universe

The nature of the dwarf galaxy population as a function of location in the cluster and within different environments is investigated. We have previously described the results of a search for low surface brightness objects in data drawn from an East-West strip of the Virgo cluster (Sabatini et al., 2003) and have compared this to a large area strip outside of the cluster (Roberts et al., 2004). In this talk I compare the East-West data (sampling sub-cluster A and outward) to new data along a North-South cluster strip that samples a different region (part of sub-cluster A, and the N,M clouds) and with data obtained for the Ursa Major cluster and fields around the spiral galaxy M101. The sample of dwarf galaxies in different environments is obtained from uniform datasets that reach central surface brightness values of ~26 B mag/arcsec^2 and an apparent B magnitude of 21 (M_B=-10 for a Virgo Cluster distance of 16 Mpc). We discuss and interpret our results on the properties and distribution of dwarf low surface brightness galaxies in the context of variuos physical processes that are thought to act on galaxies as they form and evolve.Comment: 10 pages, 3 figures, to appear in "Dark Galaxies and Lost Baryons", IAU244 conference proceeding

Primitive roles for inhibitory interneurons in developing frog spinal cord

Understanding the neuronal networks in the mammal spinal cord is hampered by the diversity of neurons and their connections. The simpler networks in developing lower vertebrates may offer insights into basic organization. To investigate the function of spinal inhibitory interneurons in Xenopus tadpoles, paired whole-cell recordings were used. We show directly that one class of interneuron, with distinctive anatomy, produces glycinergic, negative feedback inhibition that can limit firing in motoneurons and interneurons of the central pattern generator during swimming. These same neurons also produce inhibitory gating of sensory pathways during swimming. This discovery raises the possibility that some classes of interneuron, with distinct functions later in development, may differentiate from an earlier class in which these functions are shared. Preliminary evidence suggests that these inhibitory interneurons express the transcription factor engrailed, supporting a probable homology with interneurons in developing zebrafish that also express engrailed and have very similar anatomy and functions