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Does every smooth vector field on a closed three-manifold, for example the three-sphere, have a closed orbit? The answer is no, according to counterexamples by K. Kuperberg and others. On the other hand, there is a special class of vector fields, called Reeb vector fields, which are associated to contact forms. The three-dimensional case of the Weinstein conjecture asserts that every Reeb vector field on a closed oriented three-manifold has a closed orbit. This conjecture was recently proved by Taubes using Seiberg-Witten theory. We give an introduction to the Weinstein conjecture, the main ideas in Taubes's proof, and the bigger picture into which it fits.

The paper presents a novel evolutionary technique constructed as an alternative of the standard support vector machines architecture. The approach adopts the learning strategy of the latter but aims to simplify and generalize its training, by offering a transparent substitute to the initial black-box. Contrary to the canonical technique, the evolutionary approach can at all times explicitly acquire the coefficients of the decision function, without any further constraints. Moreover, in order to converge, the evolutionary method does not require the positive (semi-)definition properties for kernels within nonlinear learning. Several potential structures, enhancements and additions are proposed, tested and confirmed using available benchmarking test problems. Computational results show the validity of the new approach in terms of runtime, prediction accuracy and flexibility. doi: 10.1057/jors.2008.124 Published online 19 November 2008 Keywords: evolutionary algorithms; support vector machines; classification; regression

Near infrared (NIR) spectroscopy has been extensively used in classification problems because it is fast, reliable, cost-effective, and non-destructive. However, NIR data often have several hundred or thousand variables (wavelengths) that are highly correlated with each other. Thus, it is critical to select a few important features or wavelengths that better explain NIR data. Wavelets are popular as preprocessing tools for spectra data. Many applications perform feature selection directly, based on high-dimensional wavelet coefficients, and this can be computationally expensive. This paper proposes a two-stage scheme for the classification of NIR spectra data. In the first stage, the proposed multi-scale vertical energy thresholding procedure is used to reduce the dimension of the high-dimensional spectral data. In the second stage, a few important wavelet coefficients are selected using the proposed support vector machines gradient-recursive feature elimination. The proposed two-stage method has produced better classification performance, with higher computational efficiency, when tested on four NIR data sets. doi: 10.1057/jors.2008.179 Published online 8 April 2009 Keywords: spectra data; classification; wavelet analysis; thresholding; support vector machines; feature selection

Fluid-structure interaction (FSI) solvers based on vector extrapolation methods are discussed. The FSI solver framework builds on a Dirichlet-Neumann partitioning between general purpose fluid and structural solver. For strong coupling of the two fields vector extrapolation methods are employed to obtain a matrix free nonlinear solver. The emphasis of this presentation is on the embedding of well known vector extrapolation methods in a popular FSI solver framework and, in particular, the relation of these vector extrapolation methods to established fixed-point FSI schemes. [DOI: 10.1115/1.3057468]

A framework for the exploitation of a general non-redundant precoding for blind channel estimation in multiple-input-muliple-output (MIMO) block transmission systems with a cyclic prefix (CP) is presented. Findings reveal the achievement of accurate channel estimation with a small number of symbol blocks.

Distributed estimation of deterministic vector parameters using ad hoc wireless sensor networks (WSNs) is studied. The resultant algorithm can be decomposed into a set of simpler tasks suitable for distributed implementation.

The support vector regression framework is proposed as the basis for a new antenna array characterization technique able to model radiating structures including all the coupling effects between the elements of the array from experimental data. The obtained model can then be used to solve practical synthesis problems with high accuracy and using an appropriate cost function for template-based specification handling. A design example is presented to show the efficiency of the proposed method. Index Terms--Antenna arrays, antenna pattern synthesis, support vector regression.

This study explains how a Steinmetz compensator works. Past explanations are based on phasor diagrams and do not address the basic physical phenomena of electromagnetic energy flow. This paper uses the Poynting vector to detect and understand the process of compensation and restoration of symmetry in an unbalanced system. Index Terms--Compensation, power flow, Poynting vector (PV), three-phase systems.

This paper presents the application of a space vector modulation (SVM) strategy for a multimodule converter system that 1) enables low switching frequency, 2) eliminates/minimizes ac-side voltage harmonics, particularly low-order harmonics, and 3) provides maximum ac-side fundamental voltage component. The SVM strategy is based on a sequential sampling technique. The SVM provides harmonic cancellation/minimization by introducing appropriate phase shift for the corresponding voltage harmonics of the converter modules, while maintaining the fundamental voltage components of modules in-phase to obtain maximum ac-side voltage. The SVM eliminates the need for complicated transformer arrangement for harmonic reduction, and thus provides high degree of modularity by utilization of identical transformers for converter modules. The proposed SVM strategy is developed for a back-to-back HVDC converter station in which each converter system is composed of four identical modules. Based on a dynamic model of the system, converter controls are designed and performance of the SVM strategy in terms of converter harmonics and dynamic performance are presented. The studies are performed in time-domain, using the PSCAD/EMTDC software tool. Index Terms--Dynamic model, harmonics, HVDC, multimodule converter, space vector modulation (SVM).

A new image fusion method by employing the support value transform which used the support value to represent the salient features of image based on the support vector machines (SVMs) is described.

The Green's functions for the magnetic scalar and electric vector potentials are derived for a rectangular waveguide with dielectric-filled corrugations supporting left-hand, as well as right-hand propagation. The derivation of the Green's functions for this pair of auxiliary potentials is more involved than their electric-type counterparts. An investigation of the divergence of the electric vector potential is performed with emphasis on the waveguide transverse electric modes. As a result of the divergenceless nature of the vector potential for these modes, the scalar potential is shown to vanish within the corrugations, and the rest of the derivation proceeds by employing the spectral representation then transforming back to the spatial domain. The derived expressions are verified by considering some examples involving discontinuities that can be modeled by equivalent magnetic currents in view of the equivalence principle. Comparison with full-wave simulation commercial packages validates the current theory. Index Terms--Auxiliary potentials, corrugated waveguide, Green's function, metamaterial transmission line.

In this paper, we propose a novel algorithm for rule extraction from support vector machines (SVMs), termed SQRex-SVM. The proposed method extracts rules directly from the support vectors (SVs) of a trained SVM using a modified sequential covering algorithm. Rules are generated based on an ordered search of the most discriminative features, as measured by interclass separation. Rule performance is then evaluated using measured rates of true and false positives and the area under the receiver operating characteristic (ROC) curve (AUC). Results are presented on a number of commonly used data sets that show the rules produced by SQRex-SVM exhibit both improved generalization performance and smaller more comprehensible rule sets compared to both other SVM rule extraction techniques and direct rule learning techniques. Index Terms--Artificial Intelligence, machine learning, representations, information extraction, pattern recognition applications, SVMs.

Support vector machines (SVMs) usually need a large number of support vectors to form their output. Recently, several models have been proposed to build SVMs with a small number of basis functions, maintaining the property that their hidden-layer weights are a subset of the data (the support vectors). This property is also present in some algorithms for feedforward neural networks (FNNs) that construct the network sequentially, leading to sparse models where the number of hidden units can be explicitly controlled. An experimental study on several benchmark data sets, comparing SVMs and the aforementioned sequential FNNs, was carried out. The experiments were performed in the same conditions for all the models, and they can be seen as a comparison of SVMs and FNNs when both models are restricted to use similar hidden-layer weights. Accuracies were found to be very similar. Regarding the number of support vectors, sequential FNNs constructed models with less hidden units than standard SVMs and in the same range as 'sparse' SVMs. Computational times were lower for SVMs. Index Terms--Feedforward neural networks (FNNs), sparse models, support vector machines (SVMs).

Accurate global positioning system (GPS) vector determination is now one of the major applications in modem geodesy. Most of the errors are modeled either to smooth atmospheric effects or to remove biases, such as clock offsets. The network design criteria and the vector processing solutions are based in distances to the reference stations and in the time span of the vectors involved in the sessions. Thus, the final solutions are computed accurately and the residuals show the quality of the computations. What is not usually taken into account in the final vector computation is the bearing of the vector to be processed, as the cutoff angle is considered fixed. This note deals with the variation on the final solution due to different cutoff angles, and still more importantly to the bearing of the vector in highprecision GPS vector determination. If in the future, the International Global Navigation Satellite System Service starts to distribute orbits that are created using the ANTEX file, this elevation-angle dependency should diminish. DOI: 10.1061/(ASCE)0733-9453(2007)133:2(90) CE Database subject headings: Geographic information systems; Vector analysis; Geodetic surveys.

The problem of detecting a signal is considered by using multiple observations in the primary data and under a possible scaling inhomogeneity between the samples under test and the training samples. The new detector has provided additional robustness when compared to a detector that assumes a known signature.

In this paper support vector machine (SVM) has been used for optimization of electric field along the support insulators used in high voltage (HV) systems. To illustrate the effectiveness of SVM in optimizing electric field problems, two axi-symmetric cases have been considered one having an insulator with a contour that is quarter ellipse and the second one having a porcelain core solid insulator. The training and the test data for the SVM have been prepared by electrostatic field computation using indirect boundary element method (BEM). It is observed that once the SVM is trained it is able to give results with mean absolute error of less than 1.5 % when compared with the analytically obtained results. The SVM designed for insulator contour optimization is first trained with the results obtained from electric field computation for some predetermined contours of the arrangements under consideration. Then the trained SVM is used to produce an optimized insulator contour in such a way that the desired stress distribution can be obtained on the insulator surface. The results obtained from the present study show that the trained SVM is adequately efficient to optimize insulator contours in order to have the desired stress distribution along the insulator surface. Index Terms--Support vector machine, optimization, support insulators.

An information-theoretic alternate estimator for Bayesian method in vector auto regression models is presented.

In this paper a magnetic vector hysteresis model based on Play hysterons is presented. This model allows the evaluation of the magnetic induction vector from an imposed magnetic field vector. The proposed methodology is simple to implement and needs only a small set of experimental data. Some aspects of the model are discussed and an experimental validation is performed. Index Terms--Iron losses, magnetic hysteresis modeling, vector hysteresis.

This paper generalizes a 2-D isotropic vector hysteresis model represented by the superposition of scalar stop models. Its identification method and property of rotational hysteresis loss are discussed. The generalized vector model can control its rotational hysteresis loss. Index Terms--Identification, rotational hysteresis loss, superposition, vector stop model.

Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method. conjoint triad | support vector machine

A semantic categorization method for generic home photo is proposed. The main contribution of this paper is to exploit a two-layered classification model incorporating camera metadata with low-level features for multilabel detection. The two-layered support vector machine (SVM) classifiers operate to detect local and global photo semantics in a feed-forward way. The first layer aims to predict likelihood of predefined local photo semantics based on camera metadata and regional low-level visual features. In the second layer, one or more global photo semantics is detected based on the likelihood. To construct classifiers producing a posterior probability, we use a parametric model to fit the output of SVM classifiers to posterior probability. A concept merging process based on a set of semantic-confidence maps is also presented to cope with selecting more likelihood photo semantics on spatially overlapping local regions. Experiment was performed with 3086 photos that come from MPEG-7 visual core experiment two official databases. Results showed that the proposed method would much better capture multiple semantic meanings of home photos, compared to other similar technologies. Index Terms--Camera metadata, image classification, photo album, support vector machine.

A new subspace direction-of-arrival (DOA) estimation procedure is developed by using a noneigenvector basis and the computation is carried out by a modified version of the orthogonal auxiliary-vector (AV) filtering algorithm. The new AV basis is used for the DOA estimation with remarkable success relative to (grid-)maximum-likelihood (ML)- type estimation for uncorrelated or even highly correlated sources.

A Lloyd-type vector quantization (VQ) design algorithm and scalar quantization (SQ) are developed for quantizing the phase angles and the performance of quantized equal gain transmission (Q-EGT) is analyzed. The results have shown that although both VQ and SQ achieve the same rate of convergence as the number of feedback bits B increases, a fixed gap is present between the two.

This paper presents a new classifier called total margin-based adaptive fuzzy support vector machines (TAF-SVM) that deals with several problems that may occur in support vector machines (SVMs) when applied to the face recognition. The proposed TAF-SVM not only solves the overfitting problem resulted from the outlier with the approach of fuzzification of the penalty, but also corrects the skew of the optimal separating hyperplane due to the very imbalanced data sets by using different cost algorithm. In addition, by introducing the total margin algorithm to replace the conventional soft margin algorithm, a lower generalization error bound can be obtained. Those three functions are embodied into the traditional SVM so that the TAF-SVM is proposed and reformulated in both linear and nonlinear cases. By using two databases, the Chung Yuan Christian University (CYCU) multiview and the facial recognition technology (FERET) face databases, and using the kernel Fisher's discriminant analysis (KFDA) algorithm to extract discriminating face features, experimental results show that the proposed TAF-SVM is superior to SVM in terms of the face-recognition accuracy. The results also indicate that the proposed TAF-SVM can achieve smaller error variances than SVM over a number of tests such that better recognition stability can be obtained. Index Terms--Face recognition, kernel Fisher's discriminant analysis (KFDA), support vector machines (SVMs).

This paper addresses unsupervised change detection by proposing a proper framework for a formal definition and a theoretical study of the change vector analysis (CYA) technique. This framework, which is based on the representation of the CYA in polar coordinates, aims at: 1) introducing a set of formal definitions in the polar domain (which are linked to the properties of the data) for a better general description (and thus understanding) of the information present in spectral change vectors; 2) analyzing from a theoretical point of view the distributions of changed and unchanged pixels in the polar domain (also according to possible simplifying assumptions); 3) driving the implementation of proper preprocessing procedures to be applied to multitemporal images on the basis of the results of the theoretical study on the distributions; and 4) defining a solid background for the development of advanced and accurate automatic change-detection algorithms in the polar domain. The findings derived from the theoretical analysis on the statistical models of classes have been validated on real multispectral and multitemporal remote sensing images according to both qualitative and quantitative analyses. The results obtained confirm the interest of the proposed framework and the validity of the related theoretical analysis. Index Terms--Change detection, change vector analysis (CVA), multitemporal images, polar representation, remote sensing, spherical representation, statistical models, unsupervised techniques.

A novel approach to determine very accurately multiple parameter variations by exploiting the geometric shape of dynamic attractors in state space is presented. The approach is based on the analysis of sensitivity vector fields. These sensitivity vector fields describe changes in the state space attractor of the dynamics and system behavior when parameter variations occur. Distributed throughout the attractor in state space, these fields from a collection of snapshots for known parameter changes. Proper orthogonal decomposition of the parameter space is then employed to distinguish multiple simultaneous parametric variations. The parametric changes are reconstructed by analyzing the deformation of attractors which are characterized by means of the sensitivity vector fields. A set of basis functions in the vector space formed by the sensitivity fields is obtained and is used to successfully identity test cases involving multiple simultaneous parametric variations. The method presented is shown to be robust over a wide range of parameter variations and to perform well in the presence of noise. One of the main applications of the proposed technique is detecting multiple simultaneous damage in vibration-based structural health monitoring. [DOI: 10.1115/1.2346692]

Many researchers have studied reversible data hiding techniques in recent years and most have proposed reversible data hiding schemes that guarantee only that the original cover image can be reconstructed completely. Once the secret data are embedded in the compression domain and the receiver wants to store the cover image in a compression mode to save storage space, the receiver must extract the secret data, reconstruct the cover image, and compress the cover image again to generate compression codes. In this paper, we present a reversible data hiding scheme based on side match vector quantization (SMVQ) for digitally compressed images. With this scheme, the receiver only performs two steps to achieve the same goal: extract the secret data and reconstruct the original SMVQ compression codes. In terms of the size of the secret data, the visual quality, and the compression rate, experimental results show that the performance of our proposed scheme is better than those of other information hiding schemes for VQ-based and SMVQ-based compressed images. The experimental results further confirm the effectiveness and reversibility of the proposed scheme. Index Terms--Reversible data hiding, side match vector quantization (SMVQ), steganography.

This paper describes a modification of the vector fitting (VF) procedure for rational function approximation of frequency-domain responses. The modification greatly improves the ability of VF to relocate poles to better positions, thereby improving its convergence performance and reducing the importance of the initial pole set specification. This is achieved by replacing the high-frequency asymptotic requirement of the VF scaling function with a more relaxed condition. Calculated results demonstrate a major improvement of performance when fitting responses that are contaminated with noise. The procedure is also shown to be advantageous for wideband modeling of transmission lines, network equivalents, and transformers. Index Terms--Macromodei, rational approximation, system identification, vector fitting (VF).

Accurate diagnosis of faults in transformers can significantly enhance the safety, reliability, and economics of power systems. In the case of a fault, it has been established that the pattern of the fault currents contain a typical signature of the nature and location of the fault for a given winding. This paper describes a new approach using wavelet transform (WT) for extraction of features from the impulse test response of a transformer in time-frequency domain and support vector machine in regression mode to classify the patterns inherent in the features extracted through the WT of different fault currents. This paper also describes an approach to identify the type and location of the transformer faults accurately by analyzing experimental impulse responses that contain noise. Here, experimental impulse responses have been pre-processed with the help of wavelet-packet filters to remove the unwanted noise from the signal and thereby enhance the analyzing capability of continuous wavelet transform. Index Terms--Analog model, digital model, impulse test, support vector machine (SVM), time-frequency analysis, transformer insulation, wavelet packet (WP) transform, wavelet transform (WT).

Abstract: The local Principal Component Analysis (PCA) reduces linearly redundant components that may present in higher dimensional space. It deploys an initial guess technique which can be utilized when the [...]

Abstract: Rijndael-The AES [1-3] is 128-bit block cipher based on an elegant algebraic structure over [F.sub.2.sup.8]. This cipher employs a simple approach to its substitution, permutation (SP) operations. We take [...]

The effects of United Kingdom's new monitory policy are evaluated by using structural vector auto regression (VAR).

Abstract: This study firstly presents a survey on basic classifiers namely minimum distance classifier (MDC), vector quantization (VQ), principal component analysis (PCA), nearest neighbour (NN) and k-nearest neighbour (kNN). Then [...]

Cointegrated vector autoregression methods, which are used to examine the impact of human capital and public investments on economic growth of Portugal for the period 1960-2001, are presented.

Vector analysis, which is used to examine the viability of balance-of-payments-constrained growth model to determine the impact of foreign exchange rates on growth of Indian economy during 1950-99, is presented.

Vector auto-regression models to examine the impact of state fixed investment on private sector industrial growth in China during 1978-2000 are presented.

Structural vector auto regressions are used for measuring the effects of monetary policy innovations on the economy. The drawbacks of VAR are discussed and a factor-augmented VAR is suggested for the purpose.

Many authors have studied Federal funds futures to analyze various aspects of monetary policy. This paper utilizes the data to help in identifying a standard monetary policy Vector Auto Regression (VAR). The main idea of the paper is to presume that surprise component of the FedEs economic policy reflects the outside factors in variations of monetary policy.

Byline: Andre Varella Mollick (1) Keywords: Fixed versus flexible rates; Monetary and real shocks; Real exchange rates; Target interest rates; Vector autoregressions Abstract: This paper proposes a simple vector autoregressions (VAR) model with (real) output and exchange rate shocks on interest rates. Rather than assuming non-recursive identification schemes, we test the identifying assumption of the error term decompositions. Applying the model to quarterly data on major currencies against the U.S. dollar (USD) from 1974 to 1997, interest rate shocks explain - after 3 years - 16% of Canadian dollar/USD (CAD) real exchange rate variations and less than 2% for the mark/USD and yen/USD. Positive innovations of interest rates bring about (transitory) CAD real appreciations in differences and (permanent) appreciations in levels. Canadian real output is more explained by domestic interest rate shocks (19%) than Germany's (5%) or Japan's (0.2%). Canada is smaller than the other economies and CAD has been shown to suffer from 'fear of floating'. Our findings support the proposition that domestic shocks dominate output variance under fixed exchange rates. They are also consistent with structural interpretations of the VAR. Author Affiliation: (1) Department of Economics, ITESM-Campus Monterrey, E. Garza Sada 2501 Sur, C.P. 64849, Monterrey, N.L., Mexico Article History: Registration Date: 01/01/2003 Article note: Received: October 2002, Accepted: October 2003, JEL Classification: I wish to thank two anonymous referees of this journal for very helpful comments that improved this article. I am also grateful to Francisco Carneiro, Joao Faria, and, especially, Miguel Leon-Ledesma for comments and encouragement on previous versions of this paper. Oziel Bustos provided research assistance in early stages of this work. The usual disclaimer applies.

The author proposes a way to take account of transaction costs when using yield curves to estimate short term interest rates.

The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an [epsilon]-insensitive cost function, meaning that errors smaller than [epsilon] remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a nonsparse solution is obtained. The sparseness is imposed by pruning, i.e., recursively solving the approximation problem and subsequently omitting data that has a small error in the previous pass. However, omitting data with a small approximation error in the previous pass does not reliably predict what the error will be after the sample has been omitted. In this paper, a procedure is introduced that selects from a data set the training sample that will introduce the smallest approximation error when it will be omitted. It is shown that this pruning scheme outperforms the standard one. Index Terms--Function approximation, pruning, regression, support vector machine (SVM).

The authors create an algorithm for obtaining weights of signal filtering and extration which can replace actual observation and measurement.