Advancing the state of the art for handwritten math recognition: the CROHME competitions, 2011–2014

The CROHME competitions have helped organize the field of handwritten mathematical expression recognition. This paper presents the evolution of the competition over its first 4 years, and its contributions to handwritten math recognition, and more generally structural pattern recognition research. The competition protocol, evaluation metrics and datasets are presented in detail. Participating systems are analyzed and compared in terms of the central mathematical expression recognition tasks: (1) symbol segmentation, (2) classification of individual symbols, (3) symbol relationships and (4) structural analysis (parsing). The competition led to the development of label graphs, which allow recognition results with conflicting segmentations to be directly compared and quantified using Hamming distances. We introduce structure confusion histograms that provide frequencies for incorrect subgraphs corresponding to ground-truth label subgraphs of a given size and present structure confusion histograms for symbol bigrams (two symbols with a relationship) for CROHME 2014 systems. We provide a novel analysis combining results from competing systems at the level of individual strokes and stroke pairs; this virtual merging of system outputs allows us to more closely examine limitations for current state-of-the-art systems. Datasets along with evaluation and visualization tools produced for the competition are publicly available.     

Adaptive Stabilization of a Class of Reaction–Diffusion Systems

This paper is concerned with adaptive stabilization of a class of reaction–diffusion systems governed by a nonlinear partial differential equation of the first order in time but the fourth order in space. In the presence of bounded deterministic disturbances, the adaptive stabilizer is constructed by the concept of high-gain nonlinear output feedback and the estimation mechanism of the unknown parameters. In the control system the global asymptotic stability and the convergence of the system state to zero will be guaranteed.     

Universal noiseless and noisy data compression techniques — the state of the art

The technique of data compression demonstrates desirable properties that assure performance improvements of advanced network architectures such as ISDN, serial communication channels, data storage and data archiving. A global trend in telecommunications networks is the conversion from analogue to digital transmission. The design goal of these new networks is that voice, data, and images will be transmitted in fundamentally the same way, requiring neither prior knowledge as to the type of data being transmitted nor operator intervention. However, the bandwidth requirements for digital transmission are generally high. Spectrum bandwidth utilization is a design parameter for these systems that will benefit from the transmission of compressed data. The proposed Integrated Services Digital Network (ISDN) combines digital voice, data, and images on the same network. Since this network has limited bandwidth for transmission of information, the available bandwidth must be used as efficiently as possible.     

Evaluating recommender systems from the user’s perspective: survey of the state of the art

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users?? perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS??s ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users?? adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system??s recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system??s overall perceptive qualities and how these qualities influence users?? behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing methods.     

Identification of a Wiener–Hammerstein system using the polynomial nonlinear state space approach

In this paper, the Polynomial NonLinear State Space (PNLSS) approach is applied to model a nonlinear system with a Wiener–Hammerstein structure. To obtain good initial estimates, the best linear approximation of the system under test is first identified. Next, this linear model is extended to a polynomial nonlinear state space model to capture also the system's nonlinear behavior. The identification procedure is applied to measurement data.     

Adaptive sliding-mode controller based on the “Super-Twist” state observer for control of the Stewart platform

To provide a highly efficient control of nonlinear systems in the presence of nonmodeled dynamics and external perturbations, a new control law with feedback based on the sliding modes with an observer of the “Super-Twist” kind was proposed. For acceptable use of the continuous observer signal in the controller, presented were adaptive laws for adjustment of the control system parameters. Using the methods of Lyapunov function, system stability (convergence to a zone) was proved. This technique was proposed as an example of control and stabilization of the position of a parallel manipulator (Gough–Stewart platform). The presented mechanism with six degrees of freedom is used to control the secondary mirror of the “Large Millimeter Telescope Alfonso Serrano” situated in the state of Puebla, Mexico.     

Construction of the phenomenological model of Belousov–Zhabotinsky reaction state trajectory

The Belousov–Zhabotinsky reaction is used as an example of a self-organizing system which is easily and intelligibly observable, and experimentally accessible. The analysis does not require elaborate reconstruction of series of three-dimensional images as in the case of bird flocks, fish schools or organ behaviour. The analysis of living cells microscopic image series is even more elaborate. Moreover, the experimenter in the case of the chemical clock has full control of the mechanical constraints imposed on the system. In this, contributions are reported using both experimental and theoretical results of the BZ reaction as an experimental tool for the analysis of behaviour of the self-organizing system. We have created a state trajectory using several selected image identifiers (point information gain entropy – H_α). The H_α values define an approximate state space which may be analysed using multivariate analysis. In this report we provide results of this analysis.     

Entanglement revival can occur only when the system–environment state is not a Markov state

Markov states have been defined for tripartite quantum systems. In this paper, we generalize the definition of the Markov states to arbitrary multipartite case and find the general structure of an important subset of them, which we will call strong Markov states. In addition, we focus on an important property of the Markov states: If the initial state of the whole system–environment is a Markov state, then each localized dynamics of the whole system–environment reduces to a localized subdynamics of the system. This provides us a necessary condition for entanglement revival in an open quantum system: Entanglement revival can occur only when the system–environment state is not a Markov state. To illustrate (a part of) our results, we consider the case that the environment is modeled as classical. In this case, though the correlation between the system and the environment remains classical during the evolution, the change of the state of the system–environment, from its initial Markov state to a state which is not a Markov one, leads to the entanglement revival in the system. This shows that the non-Markovianity of a state is not equivalent to the existence of non-classical correlation in it, in general.     

Existence and uniqueness of a transient state for the coupled radiative–conductive heat transfer problem

This paper deals with existence and uniqueness results for a transient nonlinear radiative–conductive system in three-dimensional case. This system describes the heat transfer for a grey, semi-transparent and non-scattering medium with general boundary conditions. We reformulate the full transient state system as a fixed-point problem. The existence and uniqueness proof is based on Banach fixed point theorem.     

Computation of the state transition matrix for a class of delay-differential systems†

Control systems can drift into instability or, less catastrophically, exhibit resonance behaviour. One role for adaptive controllers is to learn sufficient information concerning the dominant closed-loop system mode so as to apply effective feedback to dampen these modes. In such situations the adaptive loop augments the fixed controller feedback loop. This paper presents an algorithm for adaptive resonance suppression and provides simulation results to study its behaviour in the presence of high-order unmodelled dynamics. The algorithm appears particularly useful for enhancing existing fixed controller designs.     

Adaptive control of Hammerstein–Wiener nonlinear systems

The Hammerstein–Wiener model is a block-oriented model, having a linear dynamic block sandwiched by two static nonlinear blocks. This note develops an adaptive controller for a special form of Hammerstein–Wiener nonlinear systems which are parameterized by the key-term separation principle. The adaptive control law and recursive parameter estimation are updated by the use of internal variable estimations. By modeling the errors due to the estimation of internal variables, we establish convergence and stability properties. Theoretical results show that parameter estimation convergence and closed-loop system stability can be guaranteed under sufficient condition. From a qualitative analysis of the sufficient condition, we introduce an adaptive weighted factor to improve the performance of the adaptive controller. Numerical examples are given to confirm the results in this paper.     

Adaptive practical output tracking of a class of nonlinear systems

Focus is hid on the adaptive practical output-tracking problem of a chss of nonlinear systems with high-order lower-triangular structure and uncontrollable unstable linearization. Using the modified adaptive addition of a power integrator technique as a basic tool, a new smooth adaptive state feedback controller is designed. This controller can ensure all signals of the closed-loop systems are globally bounded and output tracking error is arbitrary small.     

A numerical study of the ground state and dynamics of atomic–molecular Bose–Einstein condensates

In this paper, we numerically investigate the ground-state structure and dynamics of atomic–molecular Bose–Einstein condensates at zero temperature, which are modeled by coupled Gross–Pitaevskii equations (GPEs). To get the ground state, we evolve a gradient flow with discrete normalization numerically. To study the dynamics, we employ an efficient numerical method—the time-splitting Fourier pseudospectral method for solving the coupled GPEs. The proposed numerical methods have been numerically tested and employed in studying the mechanism on how an atomic condensate can be converted into an atomic–molecular mixture or a pure molecular condensate from an atomic condensate either in equilibrium or dynamically.     

Applicability of SAR-based wave retrieval for wind–wave interaction analysis in the fetch-limited Baltic

In this article, a method for the detection of wave field parameters from synthetic aperture radar (SAR) imagery in the fetch-limited Baltic Sea is presented. Over the Baltic Sea region, common southwest (SW) and west (W) winds induce steep waves with shorter wavelengths compared with ocean waves. Thus, with the use of previous SAR sensors (e.g. ENVISAT/ASAR), it was not possible to detect individual waves and retrieve image wave number spectra. Since the year 2007, when TerraSAR-X (TS-X) reached its orbit, high spatial resolution data is available for measuring the sea-state parameters: the individual waves up to 30 m wavelength and their refraction can be distinguished. The main objective of this work was to demonstrate the capability of detecting wave field parameter from (TS-X) imagery in the Baltic Sea. The wave field parameters obtained from the SAR imagery were compared with in situ measurements and the Simulating WAves Nearshore (SWAN) wave model. The comparison of SAR-based wave field information with buoy measurements showed high agreement in case of wave propagation direction (r = 0.95) and wavelength (r = 0.83). A significant correlation is also seen between SWAN- and SAR-derived wave propagation direction (r = 0.87) and wavelengths (r = 0.91). With the case studies, it is shown that SAR data enables one to detect land shadow effects and small-scale wave field variations in the coastal zone. It was shown that SAR data is also valuable for improving and interpreting the wave model results. In consequence of common slanting fetch cases over the Baltic Sea region, it was demonstrated that the peak wave directions differ from the mean wind directions up to 43°.     

Method of predicting the position of the energy center of the image of a laser beam using a parallel–hierarchical network

This paper presents a prediction method using a parallel–hierarchical (PH) network and hyperbolic smoothing of empirical data. The average prediction error is 0.55% for the developed method and 1.62% for neural networks; therefore, this method is more efficient as applied to real-time systems than traditional neural networks due to the use of the PH network and hyperbolic smoothing in implementing the operation of predicting the positions of energy centers of laser beam spot images for optical communication systems.     

Incorporating resting state dynamics in the analysis of encephalographic responses by means of the Mahalanobis–Taguchi strategy

The analysis of encephalographic responses has mostly been attempted via signal analytic techniques aiming at revealing the useful information from recordings which are considered as contaminated by the ubiquitous ongoing (or background) brain activity. There is continuously accumulating evidence for the existence of well-defined resting-state-networks (RSNs) in the brain, which play a crucial role in the generation of spontaneous activity and the associated neural responses. Hence, the signal plus noise is no longer a valid model and the ongoing fluctuations may influence the response.We introduce here the use of a multivariate statistical methodology, known as Mahalanobis–Taguchi (MT) strategy, which can be tailored to the spontaneous fluctuations so as to optimize the subsequent response detection. A subject-specific version of the MT strategy that combines the original methodology with a clustering algorithm for refining the training set is presented. The proposed methodology serves as an explorative tool for the detailed study of temporal patterning in brain responses.We demonstrate the potential of approach by applying it to experimental magneto-encephalographic (MEG) data. The results indicate vividly the effectiveness of the MT-strategy in analyzing and enhancing auditory responses.