Multimodal Similarity-Preserving Hashing Masci, Jonathan; Bronstein, Michael M.; Bronstein, Alexander M. ...
IEEE transactions on pattern analysis and machine intelligence,
04/2014, Letnik:
36, Številka:
4
Journal Article
Recenzirano
Odprti dostop
We introduce an efficient computational framework for hashing data belonging to multiple modalities into a single representation space where they become mutually comparable. The proposed approach is ...based on a novel coupled siamese neural network architecture and allows unified treatment of intra- and inter-modality similarity learning. Unlike existing cross-modality similarity learning approaches, our hashing functions are not limited to binarized linear projections and can assume arbitrarily complex forms. We show experimentally that our method significantly outperforms state-of-the-art hashing approaches on multimedia retrieval tasks.
Dissipative solitons Purwins, H.-G.; Bödeker, H.U.; Amiranashvili, Sh
Advances in physics,
09/2010, Letnik:
59, Številka:
5
Journal Article
Recenzirano
The present review summarizes experimental and theoretical work dealing with self-organized solitary localized structures (LSs) that are observed in spatially extended nonlinear dissipative systems ...otherwise exhibiting translational and rotational symmetry. Thereby we focus on those LSs that essentially behave like particles and that we call dissipative solitons (DSs). Such objects are also solutions of corresponding nonlinear evolution equations and it turns out that they are rather robust with respect to interaction with each other, with impurities, and with the boundary; alternatively they are generated or annihilated as a whole. By reviewing the experimental results it turns out that the richest variety of DS phenomena has been observed in electrical transport systems and optical devices. Nevertheless, DSs show up also in many other systems, among which nerve pulses in living beings are of uppermost importance in practice. In most of these systems DSs behave very similarly. The experimental results strongly suggest that phenomenon of DSs is universal. On the background of the experimental findings models for a theoretical understanding are discussed. It turns out that in a limited number of cases a straightforward quantitative description of DS patterns can be carried out. However, for the overwhelming number of systems only a qualitative approach has been successful so far. In the present review particular emphasis is laid on reaction-diffusion systems for which a kind of 'normal form' can be written down that defines a relatively large universality class comprising e.g. important electrical transport, chemical, and biological systems. For the other large class of DS carrying systems, namely optical devices, the variety of model equations is much larger and one is far away, even from a universal qualitative description. Because of this, and due to the existence of several extensive reviews on optical systems, their theoretical treatment has been mentioned only shortly. Finally, it is demonstrated that in terms of a singular perturbation approach the interaction of DSs and important aspects of their bifurcation behaviour, under certain conditions, can be described by rather simple equations. This is also true when deriving from the underlying field equations a set of ordinary differential equations containing the position coordinates of the individual DSs. Such equations represent a theoretical foundation of the experimentally observed particle-like behaviour of DSs. Though at present there is little real practical application of DSs and related patterns in an outlook we point out in which respects this might change in future. A systematic summary of a large amount of experimental and theoretical results on reaction-diffusion systems, being rather close to the subject of the present review, can also be found on the website
http://www.uni-muenster.de/Physik.AP/Purwins/Research-Summary
.
This paper studies the problem of sampled-data exponential synchronization of complex dynamical networks (CDNs) with time-varying coupling delay and uncertain sampling. By combining the ...time-dependent Lyapunov functional approach and convex combination technique, a criterion is derived to ensure the exponential stability of the error dynamics, which fully utilizes the available information about the actual sampling pattern. Based on the derived condition, the design method of the desired sampled-data controllers is proposed to make the CDNs exponentially synchronized and obtain a lower-bound estimation of the largest sampling interval. Simulation examples demonstrate that the presented method can significantly reduce the conservatism of the existing results, and lead to wider applications.
This paper investigates the problem of master-slave synchronization for neural networks with discrete and distributed delays under variable sampling with a known upper bound on the sampling ...intervals. An improved method is proposed, which captures the characteristic of sampled-data systems. Some delay-dependent criteria are derived to ensure the exponential stability of the error systems, and thus the master systems synchronize with the slave systems. The desired sampled-data controller can be achieved by solving a set of linear matrix inequalitys, which depend upon the maximum sampling interval and the decay rate. The obtained conditions not only have less conservatism but also have less decision variables than existing results. Simulation results are given to show the effectiveness and benefits of the proposed methods.
Forced oscillations may jeopardize the secure operation of power systems. To mitigate forced oscillations, locating the sources is critical. In this paper, leveraging on Sparse Identification of ...Nonlinear Dynamics (SINDy), an online purely data-driven method to locate the forced oscillation is developed. Validations in all simulated cases (in the WECC 179-bus system) and actual oscillation events (in ISO New England system) in the IEEE Task Force test cases library are carried out, which demonstrate that the proposed algorithm, requiring no model information, can accurately locate sources in most cases, even under resonance condition and when the natural modes are poorly damped. The little tuning requirement and low computational cost make the proposed method viable for online application.
In order to quantify the long-range
cross-correlations
between two time series qualitatively, we introduce a new cross-correlations test Q
CC
(m), where m is the number of degrees of freedom. If ...there are no cross-correlations between two time series, the cross-correlation test agrees well with the χ
2
(m) distribution. If the cross-correlations test exceeds the critical value of the χ
2
(m) distribution, then we say that the cross-correlations are significant. We show that if a Fourier phase-randomization procedure is carried out on a power-law cross-correlated time series, the cross-correlations test is substantially reduced compared to the case before Fourier phase randomization. We also study the effect of periodic trends on systems with power-law cross-correlations. We find that periodic trends can severely affect the quantitative analysis of long-range correlations, leading to crossovers and other spurious deviations from power laws, implying both
local
and
global
detrending approaches should be applied to properly uncover long-range power-law auto-correlations and cross-correlations in the random part of the underlying stochastic process.
This paper is intended to present a new harmonic selection technique when solving nonlinear dynamic systems with the harmonic balance method. This technique belongs to the class of method called the ...adaptive harmonic balance method (AHBM). The harmonic selection is based on the use of a tangent predictor and relies on a stepwise regression procedure that allows for a dynamic management of the number of selected harmonics via an addition or removal procedure. The efficiency of this method relative to the classical harmonic balance method (HBM) is then evaluated through examples; this later step will indicate that AHBM can significantly reduce the number of variables, thus leading to computational time savings without deteriorating solution quality.
► Selection relies upon tangent predictor and a spectral energy. ► The algorithm is not incremental. ► The number of retained Fourier coefficients varies with both dof and frequency. ► Procedure allows for time savings without deteriorating the quality of solutions.
Signal propagation in complex networks drives epidemics, is responsible for information going viral, promotes trust and facilitates moral behavior in social groups, enables the development of ...misinformation detection algorithms, and it is the main pillar supporting the fascinating cognitive abilities of the brain, to name just some examples. The geometry of signal propagation is determined as much by the network topology as it is by the diverse forms of nonlinear interactions that may take place between the nodes. Advances are therefore often system dependent and have limited translational potential across domains. Given over two decades worth of research on the subject, the time is thus certainly ripe, indeed the need is urgent, for a comprehensive review of signal propagation in complex networks. We here first survey different models that determine the nature of interactions between the nodes, including epidemic models, Kuramoto models, diffusion models, cascading failure models, and models describing neuronal dynamics. Secondly, we cover different types of complex networks and their topologies, including temporal networks, multilayer networks, and neural networks. Next, we cover network time series analysis techniques that make use of signal propagation, including network correlation analysis, information transfer and nonlinear correlation tools, network reconstruction, source localization and link prediction, as well as approaches based on artificial intelligence. Lastly, we review applications in epidemiology, social dynamics, neuroscience, engineering, and robotics. Taken together, we thus provide the reader with an up-to-date review of the complexities associated with the network’s role in propagating signals in the hope of better harnessing this to devise innovative applications across engineering, the social and natural sciences as well as to inspire future research.
In the present work, we study the dynamics of the magnetic nanodisc coupled through a magnetoelectric coupling to a ferroelectric crystal. The model of our interest is nonlinear, and we explore the ...problem under different limits of weak and strong non-linearity. By applying two electric fields with different frequencies, we control the form of the confinement potential of a ferroelectric subsystem and realize different types of dynamics. We proved that the system is more sensitive to magnetoelectric coupling in the case of double-well potential. In particular, in the case of strong non-linearity, arbitrary small values of magnetoelectric coupling lead to chaotic dynamics. In essence, magnetoelectric coupling plays a role akin to the small perturbations destroying invariant tori according to the KAM theorem. We showed that bifurcations in the system are of Hopf’s type. We observed the formation of magnetoelectric fractals in the system. In the limit of weak non-linearity, we studied a problem of parametric nonlinear resonance and enhancement of magnetic oscillations through magnetoelectric coupling.
This work presents a non-intrusive model reduction method to learn low-dimensional models of dynamical systems with non-polynomial nonlinear terms that are spatially local and that are given in ...analytic form. In contrast to state-of-the-art model reduction methods that are intrusive and thus require full knowledge of the governing equations and the operators of a full model of the discretized dynamical system, the proposed approach requires only the non-polynomial terms in analytic form and learns the rest of the dynamics from snapshots computed with a potentially black-box full-model solver. The proposed method learns operators for the linear and polynomially nonlinear dynamics via a least-squares problem, where the given non-polynomial terms are incorporated on the right-hand side. The least-squares problem is linear and thus can be solved efficiently in practice. The proposed method is demonstrated on three problems governed by partial differential equations, namely the diffusion–reaction Chafee–Infante model, a tubular reactor model for reactive flows, and a batch-chromatography model that describes a chemical separation process. The numerical results provide evidence that the proposed approach learns reduced models that achieve comparable accuracy as models constructed with state-of-the-art intrusive model reduction methods that require full knowledge of the governing equations.
•Method learns low-dimensional models of dynamical systems with non-polynomial nonlinear terms.•Requires non-polynomial terms analytically; learns the other dynamics from snapshots.•Can achieve comparable accuracy as state-of-the-art intrusive model reduction methods on numerical examples.
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