Adaptive hierarchic transformations for dynamically p-enriched slope-limiting over discontinuous Galerkin systems of generalized equations

We study a family of generalized slope limiters in two dimensions for Runge–Kutta discontinuous Galerkin (RKDG) solutions of advection-diffusion systems. We analyze the numerical behavior of these limiters applied to a pair of model problems, comparing the error of the approximate solutions, and discuss each limiter’s advantages and disadvantages. We then introduce a series of coupled p-enrichment schemes that may be used as standalone dynamic p-enrichment strategies, or may be augmented via any in the family of variable-in-p slope limiters presented.     

Adaptive mechanical-wetting lens actuated by ferrofluids

We report an adaptive mechanical-wetting lens actuated by ferrofluids. The ferrofluids works like a piston to pump liquids in and out from the lens chamber, which in turn reshapes the lens curvature and changes the focal length. Both positive and negative lenses are demonstrated experimentally. The ferrofluid-actuated mechanical-wetting lens exhibits some attractive features, such as high resolution, fast response time, low power consumption, simple structure and electronic control, weak gravity effect, and low cost. Its potential applications in medical imaging, surveillance, and commercial electronics are foreseeable.     

Theoretical analysis of imaging properties of a pressure-actuated deformable mirror for adaptive compensation of rotationally symmetrical wavefronts

Our work presents a theoretical analysis of aberration properties of a simple single-channel deformable mirror from both the aspect of modeling the shape of the mirror reflecting surface and the mechanisms of wave-front correction using such mirror. The proposed mirror can be used for a compensation of rotationally symmetrical wavefronts, e.g. focusing of optical beams, adaptive change of resonator parameters in laser technology, phase shifting, etc. The detailed analysis of possibilities of wave-front correction was performed.     

Adaptive modification of the delayed feedback control algorithm with a continuously varying time delay

We propose a simple adaptive delayed feedback control algorithm for stabilization of unstable periodic orbits with unknown periods. The state dependent time delay is varied continuously towards the period of controlled orbit according to a gradient-descent method realized through three simple ordinary differential equations. We demonstrate the efficiency of the algorithm with the Rössler and Mackey-Glass chaotic systems. The stability of the controlled orbits is proven by computation of the Lyapunov exponents of linearized equations.     

Synchronization of complex community networks with nonidentical nodes and adaptive coupling strength

In this Letter, the complex dynamical networks with community structure and nonidentical nodes are considered. The globally asymptotical synchronization of the time-delayed complex community networks onto any uniformly smooth state is studied. Some simple and useful criteria are derived by constructing an effective control scheme and adjusting automatically the adaptive coupling strength. Finally, the developed techniques are applied to two complex community networks which are respectively synchronized to a chaotic trajectory and a periodic orbit, and numerical simulations are provided to show the feasibility of the developed methods.     

Effective mechanism for social recommendation of news

Recommender systems represent an important tool for news distribution on the Internet. In this work we modify a recently proposed social recommendation model in order to deal with no explicit ratings of users on news. The model consists of a network of users which continually adapts in order to achieve an efficient news traffic. To optimize the network’s topology we propose different stochastic algorithms that are scalable with respect to the network’s size. Agent-based simulations reveal the features and the performance of these algorithms. To overcome the resultant drawbacks of each method we introduce two improved algorithms and show that they can optimize the network’s topology almost as fast and effectively as other not-scalable methods that make use of much more information.     

Adaptive nonparametric estimation for Lévy processes observed at low frequency

This article deals with adaptive nonparametric estimation for Lévy processes observed at low frequency. For general linear functionals of the Lévy measure, we construct kernel estimators, provide upper risk bounds and derive rates of convergence under regularity assumptions.     

Adaptive Lasso estimators for ultrahigh dimensional generalized linear models

Generalized linear models have been more widely used than linear models which exclude categorical variables. The penalized method becomes an effective tool to study ultrahigh dimensional generalized linear models. In this paper, we study theoretical results of the adaptive Lasso for generalized linear models in terms of diverging number of parameters and ultrahigh dimensionality. The asymptotic results are examined by several simulation studies.     

Further results on adaptive full-order and reduced-order observers for Lur’e differential inclusions

This paper is concerned with the adaptive observer design of Lur’e differential inclusions with unknown parameters. Under a relaxed assumption on nonlinear perturbation functions, a sufficient condition for the existence of an adaptive full-order observer is established. Comparing with results in the literature, the present conditions are complemented with a numerically reliable computational approach, which can be checked by means of linear matrix inequalities. Furthermore, it is shown that, under the sufficient condition, the existence of a reduced-order observer is guaranteed. Also, the reduced-order observer is designed. The effectiveness of the proposed design is illustrated via a simulation example.     

Simulation of the effect of learning on the topology of the functional connectivity of neural networks

We introduce a procedure for simulating adaptive learning in neural networks and the effect this learning has on the way in which the functional connections between the nodes of the network are established. The procedure combines two mechanisms: firstly, the gradual dilution of the network through the elimination of synaptic weights in increasing order of magnitude, thus reducing the costs of the network structure. Secondly, to train the network as it is diluted so as not to compromise its performance pursuant to the proposed task. Considering different levels of learning difficulty, we compare the topology of the functional connectivities that result from the application of this procedure with those obtained using fMRI in healthy volunteers. According to our results, the topology of functional connectivities in healthy subjects can be interpreted as the product of a learning process with a specific degree of difficulty.