bluuebunny/crossref_metadata_2025 huggingface dataset:
Genetic Screen for Monitoring Hepatitis C Virus NS3 Serine Protease Activity
Authors: Martinez Miguel Angel , Clotet Bonaventura
Published: April 2003

We have developed a genetic system to monitor the activity of the hepatitis C virus (HCV) NS3 serine protease. This genetic system is based on the bacteriophage lambda regulatory circuit where the viral repressor
c
I is specifically cleaved to initiate the switch from lysogeny to lytic infection. An HCV protease-specific target, NS5A-5B, was inserted into the lambda phage
c
I repressor. The target specificity of the HCV NS5A-5B repressor was evaluated by coexpression of this repressor with a β-galactosidase (βgal)-HCV NS3
2-181
/4
21-34
protease construct. Upon infection of
Escherichia coli
cells containing the two plasmids encoding the cI.HCV5AB-cro and the βgal-HCV NS3
2-181
/4
21-34
protease constructs, lambda phage replicated up to 8,000-fold more efficiently than in cells that did not express the HCV NS3
2-181
/4
21-34
protease. This simple, rapid, and highly specific assay can be used to monitor the activity of the HCV NS3 serine protease, and it has the potential to be used for screening specific inhibitors.
Use of High-Performance Liquid Chromatography To Study the Pharmacokinetics of Colistin Sulfate in Rats following Intravenous Administration
Authors: Li Jian , Milne Robert W. , Nation Roger L. , Turnidge John D. , Smeaton Timothy C. , Coulthard Kingsley
Published: April 2003

The pharmacokinetics of colistin was investigated using specific high-performance liquid chromatography (HPLC) to measure the concentrations of colistin and colistin A and B in plasma and urine in five rats after administration of an intravenous bolus of 1 mg of colistin sulfate/kg of body weight. There were differences in the pharmacokinetic behaviors of unbound colistin A and B. This is the first report of the use of HPLC to study the pharmacokinetics of colistin and its two major components.
Pharmacokinetic Profile of Meropenem, Administered at 500 Milligrams Every 8 Hours, in Plasma and Cantharidin-Induced Skin Blister Fluid
Authors: Maglio Dana , Teng Renli , Thyrum Per T. , Nightingale Charles H. , Nicolau David P.
Published: April 2003

The pharmacokinetic disposition of meropenem, administered at 500 mg every 8 h, in plasma and cantharidin-induced blister fluid is described. Peak meropenem concentrations in blister fluid lagged behind peak meropenem concentrations in plasma, while a lower elimination rate from blister fluid was also noted. The mean penetration of meropenem into blister fluid was 67%. The pharmacokinetic profile of meropenem in blister fluid supports the utility of this dose in the management of skin and soft tissue infections.
GEOMETRIC MODULAR ACTION AND SPACETIME SYMMETRY GROUPS
Authors: BUCHHOLZ DETLEV , DREYER OLAF , FLORIG MARTIN , SUMMERS STEPHEN J.
Published: April 2003

A condition of geometric modular action is proposed as a selection principle for physically interesting states on general space-times. This condition is naturally associated with transformation groups of partially ordered sets and provides these groups with projective representations. Under suitable additional conditions, these groups induce groups of point transformations on these space-times, which may be interpreted as symmetry groups. The consequences of this condition are studied in detail in application to two concrete space-times — four-dimensional Minkowski and three-dimensional de Sitter spaces — for which it is shown how this condition characterizes the states invariant under the respective isometry group. An intriguing new algebraic characterization of vacuum states is given. In addition, the logical relations between the condition proposed in this paper and the condition of modular covariance, widely used in the literature, are completely illuminated.
ON THE SPECTRAL AND SCATTERING THEORY OF THE SCHRÖDINGER OPERATOR WITH SURFACE POTENTIAL
Authors: CHAHROUR AYHAM , SAHBANI JAOUAD
Published: April 2003

We consider a discrete Schrödinger operator H=-Δ+V acting in ℓ2 (ℤd+1), with potential V supported by the subspace ℤd×{0}. We prove that σ (-Δ)=[-2 (d+1), 2(d+1)] is contained in the absolutely continuous spectrum of H. For this we develop a scattering theory for H. We emphasize the fact that this result applies to arbitrary potentials, so it depends on the structure of the problem rather than on a particular choice of the potential.
PRECISE HIGH ENERGY ASYMPTOTICS FOR THE INTEGRATED DENSITY OF STATES OF AN UNBOUNDED RANDOM JACOBI MATRIX
Authors: KLOPP FRÉDÉRIC
Published: April 2003

The purpose of this paper is to study the transition from the classical to the quantum asymptotics for the integrated density of states of an unbounded random Jacobi matrix. Therefore, we give precise results on the behavior of the tail of the integrated density of states near infinity. We study the evolution of these asymptotics when the decay of the tail of the distribution of the random potential increases.

Résumé. Cet article est consacré à l'étude de la transition entre le régime classique et le régime quantique pour la densité d'états intégrée d'une matrice de Jacobi aléatoire non bornée. Pour cela nous donnons des asymptotiques précises du comportement de la densité d'états intégrée au voisinage de l'infini. De plus, nous étudions le comportement de cette asymptotique lorsque la décroissance à l'infini de la distribution du potentiel aléatoire augmente.
ON THE REPRESENTATION OF Tr(e(A-λB)) AS A LAPLACE TRANSFORM
Authors: MOUSSA PIERRE
Published: April 2003

The conjecture made in 1976 by Bessis, et al. that Tr (e(A-λB)) is the Laplace transform of a positive measure when A and B are Hermitean matrices and B semi positive definite is still not solved. We review some attempts made toward the solution of the question, and provide several unpublished partial results. We also quote some mathematical problems related to this question.
THE LORENTZ–DIRAC EQUATION, I
Authors: RUF BERNHARD , SRIKANTH P.N.
Published: April 2003

The Lorentz–Dirac equation (LDE) [Formula: see text] models the point limit of the Maxwell–Lorentz equation describing the interaction of a charged extended particle with the electromagnetic field. Since (LDE) admits solutions which accelerate even if they are outside the zone of interaction, Dirac proposed to study so-called "non runaway" solutions satisfying the condition x″ (t)→0 as t → +∞. We study the scattering of particles for a localized potential barrier V(x). We show, using global bifurcation techniques, that for every T>T0 there exists a reflection solution with "returning time" T, and for every T>0 there exists a transmission solution with "transmission time" T. Furthermore, some qualitative properties of the solutions are proved; in particular, it is shown that for increasing T, these solutions spend more and more time near the maximum point s0 of V.
CONSTRUCTION OF THE SPECTRAL MEASURE FOR DEFORMED OSCILLATOR POSITION OPERATOR IN THE CASE OF UNDETERMINED HAMBURGER MOMENT PROBLEM
Authors: BORZOV V. V. , DAMASKINSKY E. V. , KULISH P. P.
Published: April 2003

The spectral measure of the position (momentum) operator X for the q-deformed oscillator is calculated in the case of the undetermined Hamburger moment problem. The presentation being given for a particular choice of generators of the q-oscillator algebra, the developed technique can be applied to other cases of undetermined moment problem as well. The spectral measure of X and its Stieltjes transformation m(z) are expressed in terms of the Jacobi matrix entries only. Using the expression found for the Stieltjes transformation m(i), the connection between the parameters labeling the spectral measures σφ and associated self-adjoint extensions Xφ is established. The spectral measures σ0 and σπ are explicitly calculated.
LAGRANGIAN AND HAMILTONIAN FORMALISM ON THE h-DEFORMED QUANTUM PLANE
Authors: CHO SUNGGOO , KANG SANG-JUN , PARK KWANG SUNG
Published: April 2003

It is known that there are only two quantum planes which are covariant under the quantum deformations of GL(2) admitting a central determinant. Contrary to the q-deformed quantum plane, the h-deformed quantum plane has a structure suitable for defining time derivatives and variations as closely as in the ordinary plane. From these we derive differential calculi including the skew-derivatives of Wess–Zumino as well as variational calculi on the quantum plane. These calculi enable us to generalize the Lagrangian and Hamiltonian formalism on the ordinary plane to the quantum plane. In particular, we construct commutation relations between noncommuting coordinates and momenta which do not depend on the initial choice of Lagrangian. We also discuss the symmetry of a Lagrangian and Noether's theorem.
EXTENSION OF ANTI-AUTOMORPHISMS AND PCT-SYMMETRY
Authors: CONTI ROBERTO , D'ANTONI CLAUDIO
Published: April 2003

We study conditions for an anti-automorphism of a C*-algebra to extend to the cross-product by a compact group dual action. As a byproduct, we show that under a few natural assumptions any PCT transformation θ of [Formula: see text], the observable net of a local QFT, extends to the canonical field net [Formula: see text]. Then we discuss the validity of the relation [Formula: see text] for every DHR morphism ρ of [Formula: see text] with finite statistics.
THE CAPPELLI–ITZYKSON–ZUBER A-D-E CLASSIFICATION
Authors: GANNON TERRY
Published: April 2003

In 1986 Cappelli, Itzykson and Zuber classified all modular invariant partition functions for the conformal field theories associated to the affine A1 algebra; they found they fall into an A-D-E pattern. Their proof was difficult and attempts to generalise it to the other affine algebras failed — in hindsight the reason is that their argument ignored most of the rich mathematical structure present. We give here the "modern" proof of their result; it is an order of magnitude simpler and shorter, and much of it has already been extended to all other affine algebras. We conclude with some remarks on the A-D-E pattern appearing in this and other RCFT classifications.
GLOBAL FOURIER INTEGRAL OPERATORS AND SEMICLASSICAL ASYMPTOTICS
Authors: LAPTEV A. , SIGAL I. M.
Published: April 2003

In this paper we introduce a class of semiclassical Fourier integral operators with global complex phases approximating the fundamental solutions (propagators) for time-dependent Schrödinger equations. Our construction is elementary, it is inspired by the joint work of the first author with Yu. Safarov and D. Vasiliev. We consider several simple but basic examples.
DEFORMED DOUBLE YANGIAN STRUCTURES
Authors: ARNAUDON D. , FRAPPAT L. , AVAN J. , ROSSI M.
Published: April 2003

Scaling limits at q → 1 of the elliptic vertex algebras [Formula: see text] are defined for any N, extending the previously known case of N = 2. They realise deformed, centrally extended double Yangian structures [Formula: see text]. As in the quantum affine algebras [Formula: see text], and quantum elliptic affine algebras [Formula: see text], these algebras contain subalgebras at critical values of the central charge c = -N -Mr (M integer, 2r = ln p/ ln q), which become Abelian when c = -N or 2r = Nh for h integer. Poisson structures and quantum exchange relations are derived for their abstract generators.
MULTI-TIME CORRELATIONS IN RELAXING QUANTUM DYNAMICAL SYSTEMS
Authors: ANDRIES JOHAN , BENATTI FABIO , De COCK MIEKE , FANNES MARK
Published: April 2003

In this paper, we consider the long time asymptotics of multi-time correlation functions for quantum dynamical systems that are sufficiently random to relax to a reference state. In particular, the evolution of such systems must have a continuous spectrum. Special attention is paid to general dynamical clustering conditions and their consequences for the structure of fluctuations of temporal averages. This approach is applied to the so-called Powers–Price shifts.
ROHLIN PROPERTY FOR SHIFT AUTOMORPHISMS
Authors: KISHIMOTO A.
Published: April 2003

We prove that the shift automorphism of the two-sided infinite tensor product of M2 ⊕ M3 has the Rohlin property. This extends the known results on the shift on UHF algebras.
A QUANTUM CRYSTAL MODEL IN THE LIGHT-MASS LIMIT: GIBBS STATES
Authors: MINLOS R. A. , VERBEURE A. , ZAGREBNOV V. A.
Published: April 2003

Ground and temperature quantum Gibbs states are constructed for a ferroelectric anharmonic quantum oscillator model with small masses. It is shown that they possess mixing properties. The construction relies on the Feynman–Kac–Nelson representation of the conditional reduced density matrices and on the cluster expansions for the corresponding Gibbs field of trajectories.
REPRESENTATIONS OF QUANTUM LORENTZ GROUP ON GELFAND SPACES
Authors: PUSZ W. , WORONOWICZ S. L.
Published: April 2003

A large class of representations of the quantum Lorentz group QLG (the one admitting Iwasawa decomposition) is found and described in detail. In a sense the class contains all irreducible unitary representations of QLG.

Parabolic subgroup P of the group QLG is introduced. It is a smooth deformation of the subgroup of SL (2, C) consisting of the upper-triangular matrices. A description of the set of all 1-dimensional representations (the characters) of P is given. It turns out that the topological structure of this set is not the same as for the parabolic subgroup of the classical Lorentz group.

The class of (in general non-unitary) representations of QLG induced by characters of its parabolic subgroup P is investigated. Representations act on spaces of smooth sections of (quantum) line boundles over the homogeneous space P\QLG (Gelfand spaces) as in the classical case. For any pair of Gelfand spaces the set of all non-zero invariant bilinear forms is described. This set is not empty only for certain pairs. We give a complete list of such pairs. Using this list we solve the problems of equivalence and irreducibility of the representation. We distinguish a class of Gelfand spaces carrying unitary representations of QLG.
INFINITESIMAL TAKESAKI DUALITY OF HAMILTONIAN VECTOR FIELDS ON A SYMPLECTIC MANIFOLD
Authors: KAWAMURA KATSUNORI
Published: April 2003

For an infinitesimal symplectic action of a Lie algebra [Formula: see text] on a symplectic manifold, we construct an infinitesimal crossed product of Hamiltonian vector fields and [Formula: see text]. We obtain its second crossed product in the case where [Formula: see text] and obtain an infinitesimal version of a Takesaki duality type theorem.
THE CHARGE QUANTUM NUMBERS OF GAUGE INVARIANT QUASI-FREE ENDOMORPHISMS
Authors: BINNENHEI CARSTEN
Published: April 2003

The representations of a group of gauge automorphisms of the canonical commutation or anticommutation relations which appear on the Hilbert spaces of isometries Hϱ implementing quasi-free endomorphisms ϱ on Fock space are studied. Such a representation, which characterizes the "charge" of ϱ in local quantum field theory, is determined by the Fock space structure of Hϱ itself: Together with a "basic" representation of the group, all higher symmetric or antisymmetric tensor powers thereof also appear. Hence ϱ is reducible unless it is an automorphism. It is further shown by the example of the massless Dirac field in two dimensions that localization and implementability of quasi-free endomorphisms are compatible with each other.
ANOMALOUS SUPERCONDUCTIVITY IN COUPLED LUTTINGER LIQUIDS
Authors: MASTROPIETRO VIERI
Published: April 2003

According to Anderson ([1], p. 54) "the equivalent of BCS theory for a fully spin-charge separated Luttinger liquid has not been formally worked out". We consider a model for two one-dimensional spinning Luttinger liquids coupled via a Cooper tunnelling Hamiltonian. We show that the partition function is the four-dimensional integral of an exponential whose exponent has an extremal point obtained solving an anomalous non-BCS self-consistence equation. If the extremal point is a global minimum the model is completely solved by the saddle point theorem and the anomalous gap generation is proved. We find that the Luttinger interaction enhances strongly Tc if the intrachain Luttinger interaction is much bigger than the interchain interaction.
A MOURRE ESTIMATE FOR A SCHRÖDINGER OPERATOR ON A BINARY TREE
Authors: ALLARD C. , FROESE R.
Published: April 2003

Let G be a binary tree with vertices V and H be a Schrödinger operator acting on ℓ2 (V). A decomposition of the space ℓ2 (V) into invariant subspaces is exhibited yielding a conjugate operator A for use in the Mourre estimate. We show that for potentials q satisfying a first order difference decay condition, a Mourre estimate for H holds.
PHASE TRANSITION IN THE SEMICLASSICAL REGIME
Authors: HELFFER BERNARD , MARTINEZ ANDRÉ
Published: April 2003

In continuation with [7] and an unpublished paper by F. Daumer [4], we would like to explore the variation of the splitting for a Schrödinger operator coming from field theory when the potential changes as a function of the temperature from a one-well situation to a double well situation. We are for the moment only able to treat the fixed dimension case. But, because the most interesting thing would be to analyze the dependence on the dimension (in the perspective of applications to statistical mechanics) we have tried to follow some of the steps in the proof with respect to the dimension. All this analysis was strongly motivated by a course by M. Kac where some heuristical discussion based on some Born–Oppenheimer analysis is presented. So our work can be considered as a first attempt for justifying this discussion.
CONTRACTIONS, GENERALIZED INÖNÜ-WIGNER CONTRACTIONS AND DEFORMATIONS OF FINITE-DIMENSIONAL LIE ALGEBRAS
Authors: WEIMAR-WOODS EVELYN
Published: April 2003

We show that any contraction is equivalent to a generalized Inönü–Wigner contraction with integer exponents, thus solving a long-standing problem to find a "simple" class of contractions that can produce all possible contractions. These contractions are given by diagonal matrices of the form T (ε)jj = εnj, nj∈ ℤ. They are ideally suited for applications. Indeed, we use this result to show that contractions are inverse to analytic deformations, thus resolving another long-standing problem. To achieve reciprocity between contractions and deformations, we have extended the definition of contractions by dropping the requirement that T (0) = lim ε → 0T (ε) exists. We give an example which proves the necessity of this extended definition.
NONSTANDARD REPRESENTATIONS OF THE CANONICAL COMMUTATION RELATIONS
Authors: YAMASHITA HIDEYASU , OZAWA MASANAO
Published: April 2003

Kelemen and Robinson reconstructed the [Formula: see text] model of Glimm and Jaffe with methods of nonstandard analysis. In order to apply nonstandard analysis to other constructions of field models systematically, this paper generalizes their nonstandard analytical methods of representing the canonical commutation relations in the framework of the theory of nonstandard unitary representations. For this purpose, such notions are introduced and examined in detail as nonstandard hulls of representations, approximate representations, and their standardizations. As applications, the following representations are reconstructed in this framework: the Segal representation, relativistic time zero fields, and the Araki–Woods representation.
THE PHASE SPACE FEYNMAN PATH INTEGRAL WITH GAUGE INVARIANCE AND ITS CONVERGENCE
Authors: ICHINOSE WATARU
Published: April 2003

Convergence of a time-slicing approximation is studied in a general way of the Feynman path integral in phase space with an electromagnetic potential. In the present paper a new approximation of the Feynman path integral in phase space, different from the familiar one in physics, is proposed so that its convergence can be shown independently of the choice of electromagnetic potentials and that gauge invariance of the Feynman path integral defined by its limit can be shown directly. It is also shown that the Feynman path integral in phase space defined above can be expressed in the form of the Feynman path integral in configuration space defined in the familiar way in physics, which Feynman himself stated heuristically.
CHARGED SECTORS, SPIN AND STATISTICS IN QUANTUM FIELD THEORY ON CURVED SPACETIMES
Authors: GUIDO D. , LONGO R. , ROBERTS J. E. , VERCH R.
Published: April 2003

The first part of this paper extends the Doplicher–Haag–Roberts theory of superselection sectors to quantum field theory on arbitrary globally hyperbolic spacetimes. The statistics of a superselection sector may be defined as in flat spacetime and each charge has a conjugate charge when the spacetime possesses non-compact Cauchy surfaces. In this case, the field net and the gauge group can be constructed as in Minkowski spacetime. The second part of this paper derives spin-statistics theorems on spacetimes with appropriate symmetries. Two situations are considered: First, if the spacetime has a bifurcate Killing horizon, as is the case in the presence of black holes, then restricting the observables to the Killing horizon together with "modular covariance" for the Killing flow yields a conformally covariant quantum field theory on the circle and a conformal spin-statistics theorem for charged sectors localizable on the Killing horizon. Secondly, if the spacetime has a rotation and PT symmetry like the Schwarzschild–Kruskal black holes, "geometric modular action" of the rotational symmetry leads to a spin-statistics theorem for charged covariant sectors where the spin is defined via the SU(2)-covering of the spatial rotation group SO(3).
COMPLETIONS OF 2-TORSION KN-ALGEBRAS OF GENUS 1
Authors: GUERRINI L.
Published: April 2003

Krichever–Novikov algebras [Formula: see text] of genus 1 with markings which are two 2-torsion points are related to a family [Formula: see text] of deformations of the Witt algebra [Formula: see text], where f varies in the space of even polynomials with vanishing constant terms. An isomorphism between the formal (resp. analytic) completion of these KN-algebras with those of the Witt algebra is proved. Central extensions of these algebras are also defined and their formal completion is proved to be isomorphic to that of the Virasoro algebra Vir.
DIFFUSION PROCESSES ON PATH SPACES WITH INTERACTIONS
Authors: HARIYA YUU , OSADA HIROFUMI
Published: April 2003

We construct dynamics on path spaces C (ℝ; ℝ) and C([-r, r];ℝ) whose equilibrium states are Gibbs measures with free potential φ and interaction potential ψ. We do this by using the Dirichlet form theory under very mild conditions on the regularity of potentials. We take the carré du champ similar to the one of the Ornstein–Uhlenbeck process on C([0, ∞);ℝ). Our dynamics are non-Gaussian because we take Gibbs measures as reference measures. Typical examples of free potentials are double-well potentials and interaction potentials are convex functions. In this case the associated infinite-volume Gibbs measures are singular to any Gaussian measures on C(ℝ;ℝ).
COMMUTATIVE GEOMETRIES ARE SPIN MANIFOLDS
Authors: RENNIE A.
Published: April 2003

In [1], Connes presented axioms governing noncommutative geometry. He went on to claim that when specialised to the commutative case, these axioms recover spin or spinc geometry depending on whether the geometry is "real" or not. We attempt to flesh out the details of Connes' ideas. As an illustration we present a proof of his claim, partly extending the validity of the result to pseudo-Riemannian spin manifolds. Throughout we are as explicit and elementary as possible.
NORM RESOLVENT CONVERGENCE TO MAGNETIC SCHRÖDINGER OPERATORS WITH POINT INTERACTIONS
Authors: TAMURA HIDEO
Published: April 2003

The Schrödinger operator with δ-like magnetic field at the origin in two dimensions is not essentially self-adjoint. It has the deficiency indices (2, 2) and each self-adjoint extension is realized as a differential operator with some boundary conditions at the origin. We here consider Schrödinger operators with magnetic fields of small support and study the norm resolvent convergence to Schrödinger operator with δ-like magnetic field. We are concerned with the boundary conditions realized in the limit when the support shrinks. The results obtained heavily depend on the total flux of magnetic field and on the resonance space at zero energy, and the proof is based on the analysis at low energy for resolvents of Schrödinger operators with magnetic potentials slowly falling off at infinity.
REMARKS ON THE GROUND STATE ENERGY OF THE SPIN-BOSON MODEL: AN APPLICATION OF THE WIGNER–WEISSKOPF MODEL
Authors: HIROKAWA MASAO
Published: April 2003

For the ground state energy of the spin-boson (SB) model, we give a new upper bound in the case with infrared singularity condition (i.e. without infrared cutoff), and a new lower bound in the case of massless bosons with infrared regularity condition. We first investigate spectral properties of the Wigner–Weisskopf (WW) model, and apply them to SB model to achieve our purpose. Then, as an extra result of the spectral analysis for WW model, we show that a non-perturbative ground state appears, and its ground state energy is so low that we cannot conjecture it by using the regular perturbation theory.
STABILITY OF GROUND STATES IN SECTORS AND ITS APPLICATION TO THE WIGNER–WEISSKOPF MODEL
Authors: ARAI ASAO , HIROKAWA MASAO
Published: April 2003

We consider two kinds of stability (under a perturbation) of the ground state of a self-adjoint operator: the one is concerned with the sector to which the ground state belongs and the other is about the uniqueness of the ground state. As an application to the Wigner–Weisskopf model which describes one mode fermion coupled to a quantum scalar field, we prove in the massive case the following: (a) For a value of the coupling constant, the Wigner–Weisskopf model has degenerate ground states; (b) for a value of the coupling constant, the Wigner–Weisskopf model has a first excited state with energy level below the bottom of the essential spectrum. These phenomena are nonperturbative.
GENERALIZED r-MATRIX STRUCTURE AND ALGEBRO-GEOMETRIC SOLUTION FOR INTEGRABLE SYSTEM
Authors: QIAO ZHIJUN
Published: April 2003

The purpose of this paper is to construct a generalized r-matrix structure of finite dimensional systems and an approach to obtain the algebro-geometric solutions of integrable nonlinear evolution equations (NLEEs). Our starting point is a generalized Lax matrix instead of the usual Lax pair. The generalized r-matrix structure and Hamiltonian functions are presented on the basis of fundamental Poisson bracket. It can be clearly seen that various nonlinear constrained (c-) and restricted (r-) systems, such as the c-AKNS, c-MKdV, c-Toda, r-Toda, c-Levi, etc, are derived from the reductions of this structure. All these nonlinear systems have r-matrices, and are completely integrable in Liouville's sense. Furthermore, our generalized structure is developed to become an approach to obtain the algebro-geometric solutions of integrable NLEEs. Finally, the two typical examples are considered to illustrate this approach: the infinite or periodic Toda lattice equation and the AKNS equation with the condition of decay at infinity or periodic boundary.
ON THE INTEGRABILITY OF A CLASS OF MONGE–AMPÈRE EQUATIONS
Authors: BRUNELLI J. C. , GÜRSES M. , ZHELTUKHIN K.
Published: April 2003

We give the Lax representations for the elliptic, hyperbolic and homogeneous second order Monge–Ampère equations. The connection between these equations and the equations of hydrodynamical type give us a scalar dispersionless Lax representation. A matrix dispersive Lax representation follows from the correspondence between sigma models, a two parameter equation for minimal surfaces and Monge–Ampère equations. Local as well nonlocal conserved densities are obtained.
THE STRUCTURE OF SECTORS ASSOCIATED WITH LONGO–REHREN INCLUSIONS II: EXAMPLES
Authors: IZUMI MASAKI
Published: April 2003

As an application of the general theory established in the first part, we determine the structure of Longo–Rehren inclusions for several systems of sectors arising from endomorphisms of the Cuntz algebras. The E6 subfactor and the Haagerup subfactor are included among these examples, and the dual principal graphs and the S and T-matrices for their Longo–Rehren inclusions are obtained. We also construct several new subfactors using endomorphisms of the Cuntz algebras, and determine their tube algebra structure.
ABSOLUTELY CONTINUOUS SPECTRUM FOR SINGULAR STARK HAMILTONIANS
Authors: BRIET P.
Published: April 2003

In this paper, we consider Stark operators perturbed by some bounded potentials. We prove that these operators are purely absolutely continuous (A-C) on some Borel subset S of R. We apply these results when the perturbation is piecewise smooth, in this case we show that the A-C spectrum is the whole real line.
A FAST FIXED-POINT ALGORITHM FOR INDEPENDENT COMPONENT ANALYSIS OF COMPLEX VALUED SIGNALS
Authors: BINGHAM ELLA , HYVÄRINEN AAPO
Published: April 2003

Separation of complex valued signals is a frequently arising problem in signal processing. For example, separation of convolutively mixed source signals involves computations on complex valued signals. In this article, it is assumed that the original, complex valued source signals are mutually statistically independent, and the problem is solved by the independent component analysis (ICA) model. ICA is a statistical method for transforming an observed multidimensional random vector into components that are mutually as independent as possible. In this article, a fast fixed-point type algorithm that is capable of separating complex valued, linearly mixed source signals is presented and its computational efficiency is shown by simulations. Also, the local consistency of the estimator given by the algorithm is proved.
A STUDY OF EARLY STOPPING AND MODEL SELECTION APPLIED TO THE PAPERMAKING INDUSTRY
Authors: EDWARDS PETER J. , MURRAY ALAN F.
Published: April 2003

This paper addresses the issues of neural network model development and maintenance in the context of a complex task taken from the papermaking industry. In particular, it describes a comparison study of early stopping techniques and model selection, both to optimise neural network models for generalisation performance. The results presented here show that early stopping via use of a Bayesian model evidence measure is a viable way of optimising performance while also making maximum use of all the data. In addition, they show that ten-fold cross-validation performs well as a model selector and as an estimator of prediction accuracy. These results are important in that they show how neural network models may be optimally trained and selected for highly complex industrial tasks where the data are noisy and limited in number.
NEURAL NETWORKS IN ANALOG HARDWARE — DESIGN AND IMPLEMENTATION ISSUES
Authors: DRAGHICI SORIN
Published: April 2003

This paper presents a brief review of some analog hardware implementations of neural networks. Several criteria for the classification of general neural networks implementations are discussed and a taxonomy induced by these criteria is presented. The paper also discusses some characteristics of analog implementations as well as some trade-offs and issues identified in the work reviewed. Parameters such as precision, chip area, power consumption, speed and noise susceptibility are discussed in the context of neural implementations. A unified review of various "VLSI friendly" algorithms is also presented. The paper concludes with some conclusions drawn from the analysis of the implementations presented.
CURVED TRAJECTORY PREDICTION USING A SELF-ORGANIZING NEURAL NETWORK
Authors: MARSHALL JONATHAN A. , SRIKANTH VISWANATH
Published: April 2003

Existing neural network models are capable of tracking linear trajectories of moving visual objects. This paper describes an additional neural mechanism, disfacilitation, that enhances the ability of a visual system to track curved trajectories. The added mechanism combines information about an object's trajectory with information about changes in the object's trajectory, to improve the estimates for the object's next probable location. Computational simulations are presented that show how the neural mechanism can learn to track the speed of objects and how the network operates to predict the trajectories of accelerating and decelerating objects.
COMPLEX-VALUED MINIMAL RESOURCE ALLOCATION NETWORK FOR NONLINEAR SIGNAL PROCESSING
Authors: DENG JIANPING , SUNDARARAJAN N. , SARATCHANDRAN P.
Published: April 2003

This paper presents a sequential learning algorithm and evaluates its performance on complex valued signal processing problems. The algorithm is referred to as Complex Minimal Resource Allocation Network (CMRAN) algorithm and it is an extension of the MRAN algorithm originally developed for online learning in real valued RBF networks. CMRAN has the ability to grow and prune the (complex) RBF network's hidden neurons to ensure a parsimonious network structure. The performance of the learning algorithm is illustrated using two applications from signal processing of communication systems. The first application considers identification of a nonlinear complex channel. The second application considers the use of CMRAN to QAM digital channel equalization problems. Simulation results presented clearly show that CMRAN is very effective in modeling and equalization with performance achieved often being superior to that of some of the well known methods.
VARIABLE SENSITIVITY IN UNSUPERVISED CLUSTERING TASKS WITH AN N-TUPLE-BASED SELF-ORGANISING NEURAL NETWORK
Authors: TAMBOURATZIS G.
Published: April 2003

This article investigates the application of the SOLNN (Self-Organising Logic Neural Network) n-tuple-based network to character recognition and image segmentation clustering tasks, where the classes consist of a large number of distinct sub-classes. It is shown that the SOLNN clustering performance and node utilisation are both improved by virtue of the distribution constraint mechanism. The clustering results are supported by means of a detailed analysis of the characteristics of each pattern space. This analysis, coupled with comparative results obtained using other self-organising models, illustrates that the SOLNN clusters the patterns in accordance to the pattern space characteristics and thus is well-suited to clustering complex datasets.
HIGHER ORDER NEURAL NETWORK GROUP MODELS FOR FINANCIAL SIMULATION
Authors: ZHANG MING , ZHANG JING CHUN , FULCHER JOHN
Published: April 2003

Real world financial data is often discontinuous and non-smooth. If we attempt to use neural networks to simulate such functions, then accuracy will be a problem. Neural network group models perform this function much better. Both Polynomial Higher Order Neural network Group (PHONG) and Trigonometric polynomial Higher Order Neural network Group (THONG) models are developed. These HONG models are open box, convergent models capable of approximating any kind of piecewise continuous function, to any degree of accuracy. Moreover they are capable of handling higher frequency, higher order non-linear and discontinuous data. Results obtained using a Higher Order Neural network Group financial simulator are presented, which confirm that HONG group models converge without difficulty, and are considerably more accurate than neural network models (more specifically, around twice as good for prediction, and a factor of four improvement in the case of simulation).
RADICAL PRUNING: A METHOD TO CONSTRUCT SKELETON RADIAL BASIS FUNCTION NETWORKS
Authors: AUGUSTEIJN MARIJKE F. , SHAW KELLY A.
Published: April 2003

Trained radial basis function networks are well-suited for use in extracting rules and explanations because they contain a set of locally tuned units. However, for rule extraction to be useful, these networks must first be pruned to eliminate unnecessary weights. The pruning algorithm cannot search the network exhaustively because of the computational effort involved. It is shown that using multiple pruning methods with smart ordering of the pruning candidates, the number of weights in a radial basis function network can be reduced to a small fraction of the original number. The complexity of the pruning algorithm is quadratic (instead of exponential) in the number of network weights. Pruning performance is shown using a variety of benchmark problems from the University of California, Irvine machine learning database.
DIGITAL VLSI ALGORITHMS AND ARCHITECTURES FOR SUPPORT VECTOR MACHINES
Authors: Anguita Davide , Boni Andrea , Ridella Sandro
Published: April 2003

In this paper, we propose some very simple algorithms and architectures for a digital VLSI implementation of Support Vector Machines. We discuss the main aspects concerning the realization of the learning phase of SVMs, with special attention on the effects of fixed-point math for computing and storing the parameters of the network. Some experiments on two classification problems are described that show the efficiency of the proposed methods in reaching optimal solutions with reaonable hardware requirements.
IMPLEMENTATION OF A PULSE COUPLED NEURAL NETWORK IN FPGA
Authors: Waldemark Joakim , Millberg Mikael , Lindblad Thomas , Waldemark Karina , Becanovic Vlatko
Published: April 2003

The Pulse Coupled neural network, PCNN, is a biologically inspired neural net and it can be used in various image analysis applications, e.g. time-critical applications in the field of image pre-processing like segmentation, filtering, etc. A VHDL implementation of the PCNN targeting FPGA was undertaken and the results presented here. The implementation contains many interesting features. By pipelining the PCNN structure a very high throughput of 55 million neuron iterations per second could be achieved. By making the coefficients re-configurable during operation, a complete recognition system could be implemented on one, or maybe two, chip(s). Reconsidering the ranges and resolutions of the constants may save a lot of hardware, since the higher resolution requires larger multipliers, adders, memories etc.
A Programmable VLSI Filter Architecture for Application in Real-Time Vision Processing Systems
Authors: Serrano-Gotarredona Teresa , Andreou Andreas G. , Linares-Barranco Bernabé
Published: April 2003

An architecture is proposed for the realization of real-time edge-extraction filtering operation in an Address-Event-Representation (AER) vision system. Furthermore, the approach is valid for any 2D filtering operation as long as the convolutional kernel F(p,q) is decomposable into an x-axis and a y-axis component, i.e. F(p,q)=H(p)V(q), for some rotated coordinate system {p,q}. If it is possible to find a coordinate system {p,q}, rotated with respect to the absolute coordinate system a certain angle, for which the above decomposition is possible, then the proposed architecture is able to perform the filtering operation for any angle we would like the kernel to be rotated. This is achieved by taking advantage of the AER and manipulating the addresses in real time. The proposed architecture, however, requires one approximation: the product operation between the horizontal component H(p) and vertical component V(q) should be able to be approximated by a signed minimum operation without significant performance degradation. It is shown that for edge-extraction applications this filter does not produce performance degradation. The proposed architecture is intended to be used in a complete vision system known as the Boundary-Contour-System and Feature-Contour-System Vision Model, proposed by Grossberg and collaborators. The present paper proposes the architecture, provides a circuit implementation using MOS transistors operated in weak inversion, and shows behavioral simulation results at the system level operation and electrical simulation and experimental results at the circuit level operation of some critical subcircuits.
VLSI Implementation of Neural Networks
Authors: Wilamowski B. M. , Binfet J. , Kaynak M. O.
Published: April 2003

Currently, fuzzy controllers are the most popular choice for hardware implementation of complex control surfaces because they are easy to design. Neural controllers are more complex and hard to train, but provide an outstanding control surface with much less error than that of a fuzzy controller. There are also some problems that have to be solved before the networks can be implemented on VLSI chips. First, an approximation function needs to be developed because CMOS neural networks have an activation function different than any function used in neural network software. Next, this function has to be used to train the network. Finally, the last problem for VLSI designers is the quantization effect caused by discrete values of the channel length (L) and width (W) of MOS transistor geometries.

Two neural networks were designed in 1.5 μm technology. Using adequate approximation functions solved the problem of activation function. With this approach, trained networks were characterized by very small errors. Unfortunately, when the weights were quantized, errors were increased by an order of magnitude. However, even though the errors were enlarged, the results obtained from neural network hardware implementations were superior to the results obtained with fuzzy system approach.
CONVERGENCE ANALYSIS OF CASCADE ERROR PROJECTION - AN EFFICIENT LEARNING ALGORITHM FOR HARDWARE IMPLEMENTATION
Authors: Duong Tuan A. , Stubberud Allen R.
Published: April 2003

In this paper, we present a mathematical foundation, including a convergence analysis, for cascading architecture neural network. Our analysis also shows that the convergence of the cascade architecture neural network is assured because it satisfies Liapunov criteria, in an added hidden unit domain rather than in the time domain. From this analysis, a mathematical foundation for the cascade correlation learning algorithm can be found. Furthermore, it becomes apparent that the cascade correlation scheme is a special case from mathematical analysis in which an efficient hardware learning algorithm called Cascade Error Projection (CEP) is proposed.

The CEP provides efficient learning in hardware and it is faster to train, because part of the weights are deterministically obtained, and the learning of the remaining weights from the inputs to the hidden unit is performed as a single-layer perceptron learning with previously determined weights kept frozen. In addition, one can start out with zero weight values (rather than random finite weight values) when the learning of each layer is commenced. Further, unlike cascade correlation algorithm (where a pool of candidate hidden units is added), only a single hidden unit is added at a time. Therefore, the simplicity in hardware implementation is also achieved.

Finally, 5- to 8-bit parity and chaotic time series prediction problems are investigated; the simulation results demonstrate that 4-bit or more weight quantization is sufficient for learning neural network using CEP. In addition, it is demonstrated that this technique is able to compensate for less bit weight resolution by incorporating additional hidden units. However, generation result may suffer somewhat with lower bit weight quantization.
AUTOMATING PARALLEL IMPLEMENTATION OF NEURAL LEARNING ALGORITHMS
Authors: RANA OMER F.
Published: April 2003

Neural learning algorithms generally involve a number of identical processing units, which are fully or partially connected, and involve an update function, such as a ramp, a sigmoid or a Gaussian function for instance. Some variations also exist, where units can be heterogeneous, or where an alternative update technique is employed, such as a pulse stream generator. Associated with connections are numerical values that must be adjusted using a learning rule, and and dictated by parameters that are learning rule specific, such as momentum, a learning rate, a temperature, amongst others. Usually, neural learning algorithms involve local updates, and a global interaction between units is often discouraged, except in instances where units are fully connected, or involve synchronous updates. In all of these instances, concurrency within a neural algorithm cannot be fully exploited without a suitable implementation strategy. A design scheme is described for translating a neural learning algorithm from inception to implementation on a parallel machine using PVM or MPI libraries, or onto programmable logic such as FPGAs. A designer must first describe the algorithm using a specialised Neural Language, from which a Petri net (PN) model is constructed automatically for verification, and building a performance model. The PN model can be used to study issues such as synchronisation points, resource sharing and concurrency within a learning rule. Specialised constructs are provided to enable a designer to express various aspects of a learning rule, such as the number and connectivity of neural nodes, the interconnection strategies, and information flows required by the learning algorithm. A scheduling and mapping strategy is then used to translate this PN model onto a multiprocessor template. We demonstrate our technique using a Kohonen and backpropagation learning rules, implemented on a loosely coupled workstation cluster, and a dedicated parallel machine, with PVM libraries.
FPNA: INTERACTION BETWEEN FPGA AND NEURAL COMPUTATION
Authors: Girau Bernard
Published: April 2003

Neural networks are usually considered as naturally parallel computing models. But the number of operators and the complex connection graph of standard neural models can not be directly handled by digital hardware devices. More particularly, several works show that programmable digital hardware is a real opportunity for flexible hardware implementations of neural networks. And yet many area and topology problems arise when standard neural models are implemented onto programmable circuits such as FPGAs, so that the fast FPGA technology improvements can not be fully exploited. Therefore neural network hardware implementations need to reconcile simple hardware topologies with complex neural architectures. The theoretical and practical framework developed in ref. 1 allows this combination thanks to some principles of configurable hardware that are applied to neural computation: Field Programmable Neural Arrays (FPNA) lead to powerful neural architectures that are easy to map onto FPGAs, thanks to a simplified topology and an original data exchange scheme. This paper shows how FPGAs have led to the definition of the FPNA computation paradigm. Then it shows how FPNAs contribute to current and future FPGA-based neural implementations by solving the general problems that are raised by the implementation of complex neural networks onto FPGAs.
ASSESSING RBF NETWORKS USING DELVE
Authors: ORR MARK , HALLAM JOHN , MURRAY ALAN , LEONARD TOM
Published: April 2003

In this paper, different methods for training radial basis function (RBF) networks for regression problems are described and illustrated. Then, using data from the DELVE archive, they are empirically compared with each other and with some other well known methods for machine learning. Each of the RBF methods performs well on at least one DELVE task, but none are as consistent as the best of the other non-RBF methods.
NEURAL FUZZY PREFERENCE INTEGRATION USING NEURAL PREFERENCE MOORE MACHINES
Authors: WERMTER STEFAN
Published: April 2003

This paper describes preference classes and preference Moore machines as a basis for integrating different hybrid neural representations. Preference classes are shown to provide a basic link between neural preferences and fuzzy representations at the preference class level. Preference Moore machines provide a link between recurrent neural networks and symbolic transducers at the preference Moore machine level. We demonstrate how the concepts of preference classes and preference Moore machines can be used to interpret neural network representations and to integrate knowledge from hybrid neural representations. One main contribution of this paper is the introduction and analysis of neural preference Moore machines and their link to a fuzzy interpretation. Furthermore, we illustrate the interpretation and combination of various neural preference Moore machines with additional real-world examples.