Scattering from Spatially Localized Chaotic and Disordered Systems

A version of scattering theory that was developed many years ago to treat nuclear scattering processes, has provided a powerful tool to study universality in scattering processes involving open quantum systems with underlying classically chaotic dynamics. Recently, it has been used to make random matrix theory predictions concerning the statistical properties of scattering resonances in mesoscopic electron waveguides and electromagnetic waveguides. We provide a simple derivation of this scattering theory and we compare its predictions to those obtained from an exactly solvable scattering model; and we use it to study the scattering of a particle wave from a random potential. This method may prove useful in distinguishing the effects of chaos from the effects of disorder in real scattering processes.Comment: 24 pages, 11 figures typos added. Published in 'Foundation of physics' February issu

Coulomb entangler and entanglement testing network for waveguide qubits

We present a small network for the testing of the entanglement of two ballistic electron waveguide qubits. The network produces different output conditional on the presence or absence of entanglement. The structure of the network allows for the determination of successful entanglement operations through the measurement of the output of a single qubit. We also present a simple model of a dynamic coulomb-like interaction and use it to describe some characteristics of a proposed scheme for the entanglement of qubits in ballistic electron waveguides.Comment: 12 pages of text plus 7 figures: total 19 page

Discriminative training for continuous speech recognition

Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully applied for automatic speech recognition. In this paper a discussion of the Minimum Classification Error and the Maximum Mutual Information objective is presented. An extended reestimation formula is used for the HMM parameter update for both objective functions. The discriminative training methods were utilized in speaker independent phoneme recognition experiments and improved the phoneme recognition rates for both discriminative training techniques

A hybrid RBF-HMM system for continuous speech recognition

A hybrid system for continuous speech recognition, consisting of a neural network with Radial Basis Functions and Hidden Markov Models is described in this paper together with discriminant training techniques. Initially the neural net is trained to approximate a-posteriori probabilities of single HMM states. These probabilities are used by the Viterbi algorithm to calculate the total scores for the individual hybrid phoneme models. The final training of the hybrid system is based on the "Minimum Classification Error\u27; objective function, which approximates the misclassification rate of the hybrid classifier, and the "Generalized Probabilistic Descent\u27; algorithm. The hybrid system was used in continuous speech recognition experiments with phoneme units and shows about 63.8% phoneme recognition rate in a speaker-independent task

Phase transition from nuclear matter to color superconducting quark matter: the effect of the isospin

We compute the mixed phase of nuclear matter and 2SC matter for different temperatures and proton fractions. After showing that the symmetry energy of the 2SC phase is, to a good approximation, three times larger than the one of the normal quark phase, we discuss and compare all the properties of the mixed phase with a 2SC component or a normal quark matter component. In particular, the local isospin densities of the nuclear and the quark component and the stiffness of the mixed phase are significantly different whether the 2SC phase or the normal quark phase are considered. If a strong diquark pairing is adopted for the 2SC phase, there is a possibility to eventually enter in the nuclear matter 2SC matter mixed phase in low energy heavy ions collisions experiments. Possible observables able to discern between the formation of the 2SC phase or the normal quark phase are finally discussed.Comment: 9 pages, 8 figure

Transport coefficients from the Boson Uehling-Uhlenbeck Equation

We derive microscopic expressions for the bulk viscosity, shear viscosity and thermal conductivity of a quantum degenerate Bose gas above $T_C$, the critical temperature for Bose-Einstein condensation. The gas interacts via a contact potential and is described by the Uehling-Uhlenbeck equation. To derive the transport coefficients, we use Rayleigh-Schrodinger perturbation theory rather than the Chapman-Enskog approach. This approach illuminates the link between transport coefficients and eigenvalues of the collision operator. We find that a method of summing the second order contributions using the fact that the relaxation rates have a known limit improves the accuracy of the computations. We numerically compute the shear viscosity and thermal conductivity for any boson gas that interacts via a contact potential. We find that the bulk viscosity remains identically zero as it is for the classical case.Comment: 10 pages, 2 figures, submitted to Phys. Rev.

A new model-discriminant training algorithm for hybrid NN-HMM systems

This paper describes a hybrid system for continuous speech recognition consisting of a neural network (NN) and a hidden Markov model (HMM). The system is based on a multilayer perceptron, which approximates the a-posteriori probability of a sequence of states, derived from semi-continuous hidden Markov models. The classification is based on a total score for each hybrid model, attained from a Viterbi search on the state probabilities. Due to the unintended discrimination between the states in each model, a new training algorithm for the hybrid neural networks is presented. The utilized error function approximates the misclassification rate of the hybrid system. The discriminance between the correct and the incorrect models is optimized during the training by the "Generalized Probabilistic Descent Algorithm\u27;, resulting in a minimum classification error. No explicit target values for the neural net output nodes are used, as in the usual backpropagation algorithm with a quadratic error function. In basic experiments up to 56% recognition rate were achieved on a vowel classification task and up to 69 % on a consonant cluster classification task