A Symmetric Approach to the Massive Nonlinear Sigma Model

In the present paper we extend to the massive case the procedure of divergences subtraction, previously introduced for the massless nonlinear sigma model (D=4). Perturbative expansion in the number of loops is successfully constructed. The resulting theory depends on the Spontaneous Symmetry Breaking parameter v, on the mass m and on the radiative correction parameter \Lambda. Fermions are not considered in the present work. SU(2) X SU(2) is the group used.Comment: 20 page

On the asymmetric zero-range in the rarefaction fan

We consider the one-dimensional asymmetric zero-range process starting from a step decreasing profile. In the hydrodynamic limit this initial condition leads to the rarefaction fan of the associated hydrodynamic equation. Under this initial condition and for totally asymmetric jumps, we show that the weighted sum of joint probabilities for second class particles sharing the same site is convergent and we compute its limit. For partially asymmetric jumps we derive the Law of Large Numbers for the position of a second class particle under the initial configuration in which all the positive sites are empty, all the negative sites are occupied with infinitely many first class particles and with a single second class particle at the origin. Moreover, we prove that among the infinite characteristics emanating from the position of the second class particle, this particle chooses randomly one of them. The randomness is given in terms of the weak solution of the hydrodynamic equation through some sort of renormalization function. By coupling the zero-range with the exclusion process we derive some limiting laws for more general initial conditions.Comment: 22 pages, to appear in Journal of Statistical Physic

Anisotropic KPZ growth in 2+1 dimensions: fluctuations and covariance structure

In [arXiv:0804.3035] we studied an interacting particle system which can be also interpreted as a stochastic growth model. This model belongs to the anisotropic KPZ class in 2+1 dimensions. In this paper we present the results that are relevant from the perspective of stochastic growth models, in particular: (a) the surface fluctuations are asymptotically Gaussian on a sqrt(ln(t)) scale and (b) the correlation structure of the surface is asymptotically given by the massless field.Comment: 13 pages, 4 figure

A Conditional Random Field for Multiple-Instance Learning

We present MI-CRF, a conditional random field (CRF) model for multiple instance learning (MIL). MI-CRF models bags as nodes in a CRF with instances as their states. It combines discriminative unary instance classifiers and pairwise dissimilarity measures. We show that both forces improve the classification performance. Unlike other approaches, MI-CRF considers all bags jointly during training as well as during testing. This makes it possible to classify test bags in an imputation setup. The parameters of MI-CRF are learned using constraint generation. Furthermore, we show that MI-CRF can incorporate previous MIL algorithms to improve on their results. MI-CRF obtains competitive results on five standard MIL datasets. 1

A distributed networked approach for fault detection of large-scale systems

Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique

Operator Formalism for Bosonic Beta-Gamma Fields on General Algebraic Curves

An operator formalism for bosonic $\beta-\gamma$ systems on arbitrary algebraic curves is introduced. The classical degrees of freedom are identified and their commutation relations are postulated. The explicit realization of the algebra formed by the fields is given in a Hilbert space equipped with a bilinear form. The construction is based on the "gaussian" representation for $\beta-\gamma$ systems on the complex sphere [Alvarez-Gaum\' e et al, Nucl. Phys. B 311 (1988) 333]. Detailed computations are provided for the two and four points correlation functions.Comment: 26 pages, plain TeX + harvma

An algebraic approach to modeling distributed multiphysics problems: The case of a DRI reactor

© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.This paper deals with the problem of modelling a chemical reactor for the Direct Reduction of Iron ore (DRI). Such a process is being increasingly promoted as a more viable alternative to the classic Blast Furnace for the production of iron from raw minerals. Due to the inherent complexity of the process and the reactor itself, its effective monitoring and control requires advanced mathematical models containing distributed-parameter components. While classical approaches such as Finite Element or Finite Differences are still reasonable options, for accuracy and computational efficiency reasons, an algebraic approach is proposed. A full multi-physical, albeit one-dimensional model is addressed and its accuracy is analysed

From interacting particle systems to random matrices

In this contribution we consider stochastic growth models in the Kardar-Parisi-Zhang universality class in 1+1 dimension. We discuss the large time distribution and processes and their dependence on the class on initial condition. This means that the scaling exponents do not uniquely determine the large time surface statistics, but one has to further divide into subclasses. Some of the fluctuation laws were first discovered in random matrix models. Moreover, the limit process for curved limit shape turned out to show up in a dynamical version of hermitian random matrices, but this analogy does not extend to the case of symmetric matrices. Therefore the connections between growth models and random matrices is only partial.Comment: 18 pages, 8 figures; Contribution to StatPhys24 special issue; minor corrections in scaling of section 2.