Cross recurrence quantification for cover song identification

There is growing evidence that nonlinear time series analysis techniques can be used to successfully characterize, classify, or process signals derived from realworld dynamics even though these are not necessarily deterministic and stationary. In the present study we proceed in this direction by addressing an important problem our modern society is facing, the automatic classification of digital information. In particular, we address the automatic identification of cover songs, i.e. alternative renditions of a previously recorded musical piece. For this purpose we here propose a recurrence quantification analysis measure that allows tracking potentially curved and disrupted traces in cross recurrence plots. We apply this measure to cross recurrence plots constructed from the state space representation of musical descriptor time series extracted from the raw audio signal. We show that our method identifies cover songs with a higher accuracy as compared to previously published techniques. Beyond the particular application proposed here, we discuss how our approach can be useful for the characterization of a variety of signals from different scientific disciplines. We study coupled Rössler dynamics with stochastically modulated mean frequencies as one concrete example to illustrate this point.

Nonlinear inverse dynamic models of gas sensing systems based on chemical sensor arrays for quantitative measurements

Gas sensing systems based on low-cost chemical sensor arrays are gaining interest for the analysis of multicomponent gas mixtures. These sensors show different problems, e.g., nonlinearities and slow time-response, which can be partially solved by digital signal processing. Our approach is based on building a nonlinear inverse dynamic system. Results for different identification techniques, including artificial neural networks and Wiener series, are compared in terms of measurement accuracy.

Application of the Mutual Information Minimization to speaker recognition / identification improvement

In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results for speaker recognition shows that a combination of several strategies can improve the recognition rates with saturated test sentences from 80% to 89.39%, while the results with clean speech (without saturation) is 87.76% for one microphone, and for speaker identification can reduce the minimum detection cost function with saturated test sentences from 6.42% to 4.15%, while the results with clean speech (without saturation) is 5.74% for one microphone and 7.02% for the other one.

Semidiscretization and long-time asymptotics of nonlinear diffusion equations

We review several results concerning the long time asymptotics of nonlinear diffusion models based on entropy and mass transport methods. Semidiscretization of these nonlinear diffusion models are proposed and their numerical properties analysed. We demonstrate the long time asymptotic results by numerical simulation and we discuss several open problems based on these numerical results. We show that for general nonlinear diffusion equations the long-time asymptotics can be characterized in terms of fixed points of certain maps which are contractions for the euclidean Wasserstein distance. In fact, we propose a new scaling for which we can prove that this family of fixed points converges to the Barenblatt solution for perturbations of homogeneous nonlinearities for values close to zero.

On the identification of monetary (and other) shocks

The purpose of this paper is twofold. First, we construct a DSGE model which spells out explicitly the instrumentation of monetary policy. The interest rate is determined every period depending on the supply and demand for reserves which in turn are affected by fundamental shocks: unforeseeable changes in cash withdrawal, autonomous factors, technology and government spending. Unexpected changes in the monetary conditions of the economy are interpreted as monetary shocks. We show that these monetary shocks have the usual effects on economic activity without the need of imposing additional frictions as limited participation in asset markets or sticky prices. Second, we show that this view of monetary policy may have important consequences for empirical research. In the model, the contemporaneous correlations between interest rates, prices and output are due to the simultaneous effect of all fundamental shocks. We provide an example where these contemporaneous correlations may be misinterpreted as a Taylor rule. In addition, we use the sign of the impact responses of all shocks on output, prices and interest rates derived from the model to identify the sources of shocks in the data.

Generalized backward doubly stochastic differential equations and SPDEs with nonlinear Neumann boundary conditions

In this paper, a new class of generalized backward doubly stochastic differential equations is investigated. This class involves an integral with respect to an adapted continuous increasing process. A probabilistic representation for viscosity solutions of semi-linear stochastic partial differential equations with a Neumann boundary condition is given.

A nonlinear threshold model for the dependence of extremes of stationary sequences

One of the main implications of the efficient market hypothesis (EMH) is that expected future returns on financial assets are not predictable if investors are risk neutral. In this paper we argue that financial time series offer more information than that this hypothesis seems to supply. In particular we postulate that runs of very large returns can be predictable for small time periods. In order to prove this we propose a TAR(3,1)-GARCH(1,1) model that is able to describe two different types of extreme events: a first type generated by large uncertainty regimes where runs of extremes are not predictable and a second type where extremes come from isolated dread/joy events. This model is new in the literature in nonlinear processes. Its novelty resides on two features of the model that make it different from previous TAR methodologies. The regimes are motivated by the occurrence of extreme values and the threshold variable is defined by the shock affecting the process in the preceding period. In this way this model is able to uncover dependence and clustering of extremes in high as well as in low volatility periods. This model is tested with data from General Motors stocks prices corresponding to two crises that had a substantial impact in financial markets worldwide; the Black Monday of October 1987 and September 11th, 2001. By analyzing the periods around these crises we find evidence of statistical significance of our model and thereby of predictability of extremes for September 11th but not for Black Monday. These findings support the hypotheses of a big negative event producing runs of negative returns in the first case, and of the burst of a worldwide stock market bubble in the second example. JEL classification: C12; C15; C22; C51 Keywords and Phrases: asymmetries, crises, extreme values, hypothesis testing, leverage effect, nonlinearities, threshold models

From creative ideas to new emerging ventures: the process of identification and exploitation among finnish design entrepreneurs

This study focuses on identification and exploitation processes among Finnish design entrepreneurs (i.e. selfemployed industrial designers). More specifically, this study strives to find out what design entrepreneurs do when they create new ventures, how venture ideas are identified and how entrepreneurial processes are organized to identify and exploit such venture ideas in the given industrial context. Indeed, what does educated and creative individuals do when they decide to create new ventures, where do the venture ideas originally come from, and moreover, how are venture ideas identified and developed into viable business concepts that are introduced on the markets? From an academic perspective: there is a need to increase our understanding of the interaction between the identification and exploitation of emerging ventures, in this and other empirical contexts. Rather than assuming that venture ideas are constant in time, this study examines how emerging ideas are adjusted to enable exploitation in dynamic market settings. It builds on the insights from previous entrepreneurship process research. The interpretations from the theoretical discussion build on the assumption that the subprocesses of identification and exploitation interact, and moreover, they are closely entwined with each other (e.g. McKelvie & Wiklund, 2004, Davidsson, 2005). This explanation challenges the common assumption that entrepreneurs would first identify venture ideas and then exploit them (e.g. Shane, 2003). The assumption is that exploitation influences identification, just as identification influences exploitation. Based on interviews with design entrepreneurs and external actors (e.g. potential customers, suppliers and collaborators), it appears as identification and exploitation of venture ideas are carried out in close interaction between a number of actors, rather than alone by entrepreneurs. Due to their available resources, design entrepreneurs have a desire to focus on identification related activities and to find external actors that take care of exploitation related activities. The involvement of external actors may have a direct impact on decisionmaking and various activities along the processes of identification and exploitation, which is something that previous research does not particularly emphasize. For instance, Bhave (1994) suggests both operative and strategic feedback from the market, but does not explain how external parties are actually involved in the decisionmaking, and in carrying out various activities along the entrepreneurial process.

Positive solutions of nonlinear problems involving the square root of the Laplacian

We consider nonlinear elliptic problems involving a nonlocal operator: the square root of the Laplacian in a bounded domain with zero Dirichlet boundary conditions. For positive solutions to problems with power nonlinearities, we establish existence and regularity results, as well as a priori estimates of Gidas-Spruck type. In addition, among other results, we prove a symmetry theorem of Gidas-Ni-Nirenberg type.

Rigorous derivation of a nonlinear diffusion equation as fast-reaction limit of a continuous coagulation-fragmentation model with diffusion

Weak solutions of the spatially inhomogeneous (diffusive) Aizenmann-Bak model of coagulation-breakup within a bounded domain with homogeneous Neumann boundary conditions are shown to converge, in the fast reaction limit, towards local equilibria determined by their mass. Moreover, this mass is the solution of a nonlinear diffusion equation whose nonlinearity depends on the (size-dependent) diffusion coefficient. Initial data are assumed to have integrable zero order moment and square integrable first order moment in size, and finite entropy. In contrast to our previous result [CDF2], we are able to show the convergence without assuming uniform bounds from above and below on the number density of clusters.

A mixed finite element method for nonlinear diffusion equations

We propose a mixed finite element method for a class of nonlinear diffusion equations, which is based on their interpretation as gradient flows in optimal transportation metrics. We introduce an appropriate linearization of the optimal transport problem, which leads to a mixed symmetric formulation. This formulation preserves the maximum principle in case of the semi-discrete scheme as well as the fully discrete scheme for a certain class of problems. In addition solutions of the mixed formulation maintain exponential convergence in the relative entropy towards the steady state in case of a nonlinear Fokker-Planck equation with uniformly convex potential. We demonstrate the behavior of the proposed scheme with 2D simulations of the porous medium equations and blow-up questions in the Patlak-Keller-Segel model.

A numerical solver for a nonlinear Fokker-Planck equation representation of neuronal network dynamics

To describe the collective behavior of large ensembles of neurons in neuronal network, a kinetic theory description was developed in [13, 12], where a macroscopic representation of the network dynamics was directly derived from the microscopic dynamics of individual neurons, which are modeled by conductance-based, linear, integrate-and-fire point neurons. A diffusion approximation then led to a nonlinear Fokker-Planck equation for the probability density function of neuronal membrane potentials and synaptic conductances. In this work, we propose a deterministic numerical scheme for a Fokker-Planck model of an excitatory-only network. Our numerical solver allows us to obtain the time evolution of probability distribution functions, and thus, the evolution of all possible macroscopic quantities that are given by suitable moments of the probability density function. We show that this deterministic scheme is capable of capturing the bistability of stationary states observed in Monte Carlo simulations. Moreover, the transient behavior of the firing rates computed from the Fokker-Planck equation is analyzed in this bistable situation, where a bifurcation scenario, of asynchronous convergence towards stationary states, periodic synchronous solutions or damped oscillatory convergence towards stationary states, can be uncovered by increasing the strength of the excitatory coupling. Finally, the computation of moments of the probability distribution allows us to validate the applicability of a moment closure assumption used in [13] to further simplify the kinetic theory.

Development of specific ITS markers for plant DNA identification within herbivorous insects

DNA based techniques have proved to be very useful methods to study trophic relationships 17 between pests and their natural enemies. However, most predators are best defined as omnivores, 18 and the identification of plant-specific DNA should also allow the identification of the plant 19 species the predators have been feeding on. In this study, a PCR approach based on the 20 development of specific primers was developed as a self-marking technique to detect plant DNA 21 within the gut of one heteropteran omnivorous predator (Macrolophus pygmaeus) and two 22 lepidopteran pest species (Helicoverpa armigera and Tuta absoluta). Specific tomato primers 23 were designed from the ITS 1-2 region, which allowed the amplification of a tomato DNA 24 fragment of 332 bp within the three insect species tested in all cases (100% of detection at t = 0) 25 and did not detect DNA of other plants nor of the starved insects. Plant DNA half-lives at 25ºC 26 ranged from 5.8h, to 27.7h and 28.7h within M. pygmaeus, H. armigera and T. absoluta, 27 respectively. Tomato DNA detection within field collected M. pygmaeus suggests dietary mixing 28 in this omnivorous predator and showed a higher detection of tomato DNA in females and 29 nymphs than males. This study provides a useful tool to detect and to identify plant food sources 30 of arthropods and to evaluate crop colonization from surrounding vegetation in conservation 31 biological control programs.

Analysis of nonlinear noisy integrate & fire neuron models: blow-up and steady states

Nonlinear Noisy Leaky Integrate and Fire (NNLIF) models for neurons networks can be written as Fokker-Planck-Kolmogorov equations on the probability density of neurons, the main parameters in the model being the connectivity of the network and the noise. We analyse several aspects of the NNLIF model: the number of steady states, a priori estimates, blow-up issues and convergence toward equilibrium in the linear case. In particular, for excitatory networks, blow-up always occurs for initial data concentrated close to the firing potential. These results show how critical is the balance between noise and excitatory/inhibitory interactions to the connectivity parameter.

Music identification

L'evolució ens els últims decennis de les possibilitats relacionades amb les tecnologies de la informació han provocat l'aparició de diferents camps, entre ells l'anomenat “recuperació de música basant-se en el contingut”, que tracta de calcular la similitud entre diferents sons. En aquest projecte hem fet una recerca sobre els diferents mètodes que existeixen avui en dia, i posteriorment n'hem comparat tres, un basat en característiques del so, un basat en la transformada discreta del cosinus, i un que combina els dos anteriors. Els resultats han mostrat, que el basat en la transformada de Fourier és el més fiable.