Bursting synchronization in networks with long-range coupling mediated by a diffusing chemical substance

Many networks of physical and biological interest are characterized by a long-range coupling mediated by a chemical which diffuses through a medium in which oscillators are embedded. We considered a one-dimensional model for this effect for which the diffusion is fast enough so as to be implemented through a coupling whose intensity decays exponentially with the lattice distance. In particular, we analyzed the bursting synchronization of neurons described by two timescales (spiking and bursting activity), and coupled through such a long-range interaction network. One of the advantages of the model is that one can pass from a local (Laplacian) type of coupling to a global (all-to-all) one by varying a single parameter in the interaction term. We characterized bursting synchronization using an order parameter which undergoes a transition as the coupling parameters are changed through a critical value. We also investigated the role of an external time-periodic signal on the bursting synchronization properties of the network. We show potential applications in the control of pathological rhythms in biological neural networks.     

Existence of a Weak Solution in L p to the Vortex-Wave System

The vortex-wave system is a coupling of the two-dimensional vorticity equation with the point-vortex system. It is a model for the motion of a finite number of concentrated vortices moving in a distributed vorticity background. In this article, we prove existence of a weak solution to this system with an initial background vorticity in L ^p , p>2, up to the time of first collision of point vortices.     

Crop rotation scheduling with adjacency constraints

In this article we propose a 0-1 optimization model to determine a crop rotation schedule for each plot in a cropping area. The rotations have the same duration in all the plots and the crops are selected to maximize plot occupation. The crops may have different production times and planting dates. The problem includes planting constraints for adjacent plots and also for sequences of crops in the rotations. Moreover, cultivating crops for green manuring and fallow periods are scheduled into each plot. As the model has, in general, a great number of constraints and variables, we propose a heuristics based on column generation. To evaluate the performance of the model and the method, computational experiments using real-world data were performed. The solutions obtained indicate that the method generates good results.     

Integrating algebra and proof in high school mathematics: An exploratory study

Frequently, in the US students’ work with proofs is largely concentrated to the domain of high school geometry, thus providing students with a distorted image of what proof entails, which is at odds with the central role that proof plays in mathematics. Despite the centrality of proof in mathematics, there is a lack of studies addressing how to integrate proof into other mathematical domains. In this paper, we discuss a teaching experiment designed to integrate algebra and proof in the high school curriculum. Algebraic proof was envisioned as the vehicle that would provide high school students the opportunity to learn not only about proof in a context other than geometry, but also about aspects of algebra. Results from the experiment indicate that students meaningfully learned about aspects of both algebra and proof in that they produced algebraic proofs involving multiple variables, based on conjectures they themselves generated.     

Study of the Cost Functional for Free Material Design Problems

We study properties of the cost functional arising in free material optimization problems, with special emphasis on semicontinuity and its relation to convexity.     

A spatial scan statistic for case event data based on connected components

We propose a new spatial scan statistic based on graph theory as a method for detecting irregularly-shaped clusters of events over space. A graph-based method is proposed for identifying potential clusters in spatial point processes. It relies on linking the events closest than a given distance and thus defining a graph associated to the point process. The set of possible clusters is then restricted to windows including the connected components of the graph. The concentration in each of these possible clusters is measured through classical concentration indices based on likelihood ratio and also through a new concentration index which does not depend on any alternative hypothesis. These graph-based spatial scan tests seem to be very powerful against any arbitrarily-shaped cluster alternative, whatever the dimension of the data. These results have applications in various fields, such as the epidemiological study of rare diseases or the analysis of astrophysical data.     

Wavelet-based dynamic time warping

Dynamic Time Warping (DTW), a pattern matching technique traditionally used for restricted vocabulary speech recognition, is based on a temporal alignment of the input signal with the template models. The principal drawback of DTW is its high computational cost as the lengths of the signals increase. This paper shows extended results over our previously published conference paper, which introduces an optimized version of the DTW that is based on the Discrete Wavelet Transform (DWT).     

The existence of Thom classes

In this paper we consider the category of unstable modules over the Steenrod algebra. We prove the existence of finite primary decompositions in this category. Moreover, we prove the existence of Thom classes in noetherian unstable modules, i.e., elements that generate cyclic unstable submodules that are closed under the action of the Steenrod algebra. This in turn leads to the proof of the prime filtration theorem in the category of noetherian unstable modules. As an application we present a proof of the Landweber-Stong conjecture (now a theorem by D. Bourguiba and S. Zarati) that does not make use of the classification of injectives.     

Sequential Bayesian learning for stochastic volatility with variance‐gamma jumps in returns

In this work, we investigate sequential Bayesian estimation for inference of stochastic volatility with variance‐gamma (SVVG) jumps in returns. We develop an estimation algorithm that combines the sequential learning auxiliary particle filter with the particle learning filter. Simulation evidence and empirical estimation results indicate that this approach is able to filter latent variances, identify latent jumps in returns, and provide sequential learning about the static parameters of SVVG. We demonstrate comparative performance of the sequential algorithm and off‐line Markov Chain Monte Carlo in synthetic and real data applications.     

α-contractions and attractors for dissipative semilinear hyperbolic equations and systems

Summary In this paper we discuss the existence of compact attractor for the abstract semilinear evolution equation u=Au+f(t, u); the results are applied to damped partial differential equations of hyperbolic type. Our approach is a combination of Liapunov method with the theory of -eontractions.