RealTracer—Tools for Measuring the Performance of RealVideo on the Internet

The increase in high-bandwidth connections and high-speed computers has spurred the growth of streaming media across the Internet. While there have been a number of studies measuring the performance of traditional Internet traffic, there have not been sufficient empirical measurements ofvideo performance especially for commercial videos across the Internet. The lack of empirical workthat measures streaming video traffic may arise from the lack of effective video performance measurement tools. In this paper, we present RealTracer, a set of tools for measuring the performance of RealNetworks Video. RealTracer includes RealTracker, a customized video playe that plays RealNetworks Video from pre-selected playlist and records user-centric video performance information. RealTracer also includes RealData, a data analysis tool that helps manage,parse and analyze data captured by RealTracker. We describe the software architecture and usage ofRealTracker and the usage of RealData, both publicly available for download. To illustrate the useof RealTracer, we present some results from a study that used RealTracker to measure RealVideo performance across the Internet. Using RealData, that study made several contributions to better understanding the performance of streaming video on the Internet. Typical RealVideos have high quality, achieving an average frame rate of 10 frames per second and very smooth playout, but very few videos achieve full-motion frame rates. Overall video performance is most influenced by the bandwidth of the end-user connection to the Internet, but high-bandwidth Internet connections are pushing the video performance bottleneck closer to the server. Yubing Wang earned M.S. in Electrical Engineering from University of Science and Technology of China in 1993 and M.S. in Computer Science from WPI in 2001. He is currently working in EMC Corp. as a principal software engineer, participating in the developments of several EMC NAS products. He is also a Ph.D. candidate in Computer Science of WPI. His primary research interests include multimedia networking and distributed file system. Mark Claypool earned M.S. and Ph.D. degrees from the University of Minnesota in 1993 and 1997, respectively. Dr. Claypool joined the Computer Science department of WPI in 1997, receiving tenure and promotion to Associate Professor in 2004. He is also the Director of the Interactive Media and Game Development major at WPI, a 4-year degree in the principles of interactive applications and computer-based game development. His primary research interests include multimedia networking, congestion control, and network games.     

Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition

This paper presents a novel learning approach for Face Recognition by introducing Optimal Local Basis. Optimal local bases are a set of basis derived by reinforcement learning to represent the face space locally. The reinforcement signal is designed to be correlated to the recognition accuracy. The optimal local bases are derived then by finding the most discriminant features for different parts of the face space, which represents either different individuals or different expressions, orientations, poses, illuminations, and other variants of the same individual. Therefore, unlike most of the existing approaches that solve the recognition problem by using a single basis for all individuals, our proposed method benefits from local information by incorporating different bases for its decision. We also introduce a novel classification scheme that uses reinforcement signal to build a similarity measure in a non-metric space. Experiments on AR, PIE, ORL and YALE databases indicate that the proposed method facilitates robust face recognition under pose, illumination and expression variations. The performance of our method is compared with that of Eigenface, Fisherface, Subclass Discriminant Analysis, and Random Subspace LDA methods as well.     

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as “in-the-wild”). This is partially attributed to the fact that comprehensive “in-the-wild” benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking “in-the-wild”. Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.     

Design and Performance Evaluation of Residual Generators for the FDI of an Aircraft

In this work, several procedures for the fault detection and isolation （FDI） on general aviation aircraft sensors are presented. In order to provide a comprehensive wide-pectrum treatment, both linear and nonlinear, model-based and data-driven methodologies are considered. The main contributions of the paper are related to the development of both FDI polynomial method （PM） and FDI scheme based on the nonLinear geometric approach （NLGA）. As to the PM, the obtained results highlight a good trade-off between solution complexity and resulting performances. Moreover, the proposed PM is especially useful when robust solutions are required for minimising the effects of modelling errors and noise, while maximising fault sensitivity. As to the NLGA, the proposed work is the first development and robust application of the NLGA to an aircraft model in flight conditions characterised by tight-oupled longitudinal and lateral dynamics. In order to verify the robustness of the residual generators related to the previous FDI techniques, the simulation results adopt a typical aircraft reference trajectory embedding several steady-tate flight conditions, such as straight flight phases and coordinated turns. Moreover, the simulations are performed in the presence of both measurement and modelling errors. Finally, extensive simulations are used for assessing the overall capabilities of the developed FDI schemes and a comparison with neural networks （NN） and unknown input Kalman filter （UIKF） diagnosis methods is performed.     

Methods for the combination of kernel matrices within a support vector framework

The problem of combining different sources of information arises in several situations, for instance, the classification of data with asymmetric similarity matrices or the construction of an optimal classifier from a collection of kernels. Often, each source of information can be expressed as a similarity matrix. In this paper we propose a new class of methods in order to produce, for classification purposes, a single kernel matrix from a collection of kernel (similarity) matrices. Then, the constructed kernel matrix is used to train a Support Vector Machine (SVM). The key ideas within the kernel construction are twofold: the quantification, relative to the classification labels, of the difference of information among the similarities; and the extension of the concept of linear combination of similarity matrices to the concept of functional combination of similarity matrices. The proposed methods have been successfully evaluated and compared with other powerful classifiers and kernel combination techniques on a variety of artificial and real classification problems.     

Human Face Recognition and the Face Image Set′s Topology

If we consider an n × n image as an n²-dimensional vector, then images of faces can be considered as points in this n²-dimensional image space. Our previous studies of physical transformations of the face, including translation, small rotations, and illumination changes, showed that the set of face images consists of relatively simple connected subregions in image space. Consequently linear matching techniques can be used to obtain reliable face recognition. However, for more general transformations, such as large rotations or scale changes, the face subregions become highly non-convex. We have therefore developed a scale-space matching technique that allows us to take advantage of knowledge about important geometrical transformations and about the topology of the face subregion in image space. While recognition of faces is the focus of this paper, the algorithm is sufficiently general to be applicable to a large variety of object recognition tasks     

Adaptive Multiresolution Methods for the Simulation of Waves in Excitable Media

We present fully adaptive multiresolution methods for a class of spatially two-dimensional reaction-diffusion systems which describe excitable media and often give rise to the formation of spiral waves. A novel model ingredient is a strongly degenerate diffusion term that controls the degree of spatial coherence and serves as a mechanism for obtaining sharper wave fronts. The multiresolution method is formulated on the basis of two alternative reference schemes, namely a classical finite volume method, and Barkley’s approach (Barkley in Phys. D 49:61–70, 1991), which consists in separating the computation of the nonlinear reaction terms from that of the piecewise linear diffusion. The proposed methods are enhanced with local time stepping to attain local adaptivity both in space and time. The computational efficiency and the numerical precision of our methods are assessed. Results illustrate that the fully adaptive methods provide stable approximations and substantial savings in memory storage and CPU time while preserving the accuracy of the discretizations on the corresponding finest uniform grid.     

A New Family of Mixed Methods for the Reissner-Mindlin Plate Model Based on a System of First-Order Equations

The mixed method for the biharmonic problem introduced in (Behrens and Guzmán, SIAM J. Numer. Anal., 2010) is extended to the Reissner-Mindlin plate model. The Reissner-Mindlin problem is written as a system of first order equations and all the resulting variables are approximated. However, the hybrid form of the method allows one to eliminate all the variables and have a final system only involving the Lagrange multipliers that approximate the transverse displacement and rotation at the edges of the triangulation. Mixed finite element spaces for elasticity with weakly imposed symmetry are used to approximate the bending moment matrix. Optimal estimates independent of the plate thickness are proved for the transverse displacement, rotations and bending moments. A post-processing technique is provided for the displacement and rotations variables and we show numerically that they converge faster than the original approximations.     

Recent Developments in Spectral Stochastic Methods for the Numerical Solution of Stochastic Partial Differential Equations

Uncertainty quantification appears today as a crucial point in numerous branches of science and engineering. In the last two decades, a growing interest has been devoted to a new family of methods, called spectral stochastic methods, for the propagation of uncertainties through physical models governed by stochastic partial differential equations. These approaches rely on a fruitful marriage of probability theory and approximation theory in functional analysis. This paper provides a review of some recent developments in computational stochastic methods, with a particular emphasis on spectral stochastic approaches. After a review of different choices for the functional representation of random variables, we provide an overview of various numerical methods for the computation of these functional representations: projection, collocation, Galerkin approaches…. A detailed presentation of Galerkin-type spectral stochastic approaches and related computational issues is provided. Recent developments on model reduction techniques in the context of spectral stochastic methods are also discussed. The aim of these techniques is to circumvent several drawbacks of spectral stochastic approaches (computing time, memory requirements, intrusive character) and to allow their use for large scale applications. We particularly focus on model reduction techniques based on spectral decomposition techniques and their generalizations. This work is supported by the French National Research Agency (grant ANR-06-JCJC-0064) and by GdR MoMaS with partners ANDRA, BRGM, CEA, CNRS, EDF, IRSN.     

Abstraction as a basis for the computational interpretation of creative cross-modal metaphor

Various approaches to computational metaphor interpretation are based on pre-existing similarities between source and target domains and/or are based on metaphors already observed to be prevalent in the language. This paper addresses similarity-creating cross-modal metaphoric expressions. It is shown how the “abstract concept as object” (or reification) metaphor plays a central role in a large class of metaphoric extensions. The described approach depends on the imposition of abstract ontological components, which represent source concepts, onto target concepts. The challenge of such a system is to represent both denotative and connotative components which are extensible, together with a framework of general domains between which such extensions can conceivably occur. An existing ontology of this kind, consistent with some mathematic concepts and widely held linguistic notions, is outlined. It is suggested that the use of such an abstract representation system is well adapted to the interpretation of both conventional and unconventional metaphor that is similarity-creating.     

Performance Evaluation of Optimal Linear Systems†

The parameter variation problem of optimal linear control systems with quadratic performance indices is considered. The change in performance due to persistent changes in parameters is easily evaluated by considering the performance index as the Lyapunov function. Assuming the control law to be unchanged, parameter variations that cause system performance deterioration or improvement, are studied on the parameter plane. The bounds on performance are also given. The last example deals with a piecewise linear system.     

Application of Discontinuous Galerkin Methods for Reaction-Diffusion Systems in Developmental Biology

Nonlinear reaction-diffusion systems which are often employed in mathematical modeling in developmental biology are usually highly stiff in both diffusion and reaction terms. Moreover, they are typically considered on multidimensional complex geometrical domains because of complex shapes of embryos. We overcome these computational challenges by combining discontinuous Galerkin (DG) finite element methods with Strang type symmetrical operator splitting technique, on triangular meshes. This allows us to avoid directly solving a coupled nonlinear system, as is necessary with the standard implicit schemes. Numerical solutions of two reaction-diffusion systems, the well-studied Schnakenberg model, which has been applied to several problems in developmental biology, and a new biologically based system for skeletal pattern formation in the vertebrate limb, are presented to demonstrate effects of various domain geometries on the resulting biological patterns.     

Can We Talk? Methods for Evaluation and Training of Spoken Dialogue Systems

There is a strong relationship between evaluation and methods for automatically training language processing systems, where generally the same resource and metrics are used both to train system components and to evaluate them. To date, in dialogue systems research, this general methodology is not typically applied to the dialogue manager and spoken language generator. However, any metric for evaluating system performance can be used as a feedback function for automatically training the system. This approach is motivated with examples of the application of reinforcement learning to dialogue manager optimization, and the use of boosting to train the spoken language generator.     

Hybridizable Discontinuous Galerkin Methods for Timoshenko Beams

In this paper, we introduce a new class of discontinuous Galerkin methods for Timoshenko beams. The main feature of these methods is that they can be implemented in an efficient way through a hybridization procedure which reduces the globally coupled unknowns to approximations to the displacement and bending moment at the element boundaries. After displaying the methods, we obtain conditions under which they are well defined. We then compare these new methods with the already existing discontinuous Galerkin methods for Timoshenko beams. Finally, we display extensive numerical results to ascertain the influence of the stabilization parameters on the accuracy of the approximation. In particular, we find specific choices for which all the variables, namely, the displacement, the rotation, the bending moment and the shear force converge with the optimal order of k+1 when each of their approximations are taken to be piecewise polynomial of degree k≥0.     

A High Order Compact Scheme for the Pure-Streamfunction Formulation of the Navier-Stokes Equations

In this paper we continue the study, which was initiated in (Ben-Artzi et al. in Math. Model. Numer. Anal. 35(2):313–303, 2001; Fishelov et al. in Lecture Notes in Computer Science, vol. 2667, pp. 809–817, 2003; Ben-Artzi et al. in J. Comput. Phys. 205(2):640–664, 2005 and SIAM J. Numer. Anal. 44(5):1997–2024, 2006) of the numerical resolution of the pure streamfunction formulation of the time-dependent two-dimensional Navier-Stokes equation. Here we focus on enhancing our second-order scheme, introduced in the last three afore-mentioned articles, to fourth order accuracy. We construct fourth order approximations for the Laplacian, the biharmonic and the nonlinear convective operators. The scheme is compact (nine-point stencil) for the Laplacian and the biharmonic operators, which are both treated implicitly in the time-stepping scheme. The approximation of the convective term is compact in the no-leak boundary conditions case and is nearly compact (thirteen points stencil) in the case of general boundary conditions. However, we stress that in any case no unphysical boundary condition was applied to our scheme. Numerical results demonstrate that the fourth order accuracy is actually obtained for several test-cases.     

2-D EAG Method for the Recognition of Hand-Printed Chinese Characters

A method, called Two-Dimensional Extended Attribute Grammars (2-DEAGs). for the recognition ofhand-printed Chinese characters is presented. This method uses directly two dimensional information, and pro-vides a scheme for dealing with various kinds of specific cases in a uniform way. In this method, componentsare drawn in guided and redundant way and reductions are made level by level just in accordance with the com-ponent combination relations of Chinese characters. The method provides also polysemous grammars,coexisting grammars and structure inferrings which constrain redundant recognition by comparison among similarcharacters or components and greatly increase the tolerance ability to distortion.     

Approximation Algorithms for Reconstructing the Duplication History of Tandem Repeats

Computing the duplication history of a tandem repeated region is an important problem in computational biology (Fitch in Genetics 86:623–644, 1977; Jaitly et al. in J. Comput. Syst. Sci. 65:494–507, 2002; Tang et al. in J. Comput. Biol. 9:429–446, 2002). In this paper, we design a polynomial-time approximation scheme (PTAS) for the case where the size of the duplication block is 1. Our PTAS is faster than the previously best PTAS in Jaitly et al. (J. Comput. Syst. Sci. 65:494–507, 2002). For example, to achieve a ratio of 1.5, our PTAS takes O(n ⁵) time while the PTAS in Jaitly et al. (J. Comput. Syst. Sci. 65:494–507, 2002) takes O(n ¹¹) time. We also design a ratio-6 polynomial-time approximation algorithm for the case where the size of each duplication block is at most 2. This is the first polynomial-time approximation algorithm with a guaranteed ratio for this case. Part of work was done during a Z.-Z. Chen visit at City University of Hong Kong.     

2-D EAG Method for the Recognition of Hand-Printed Chinese Characters

A method,called Two-Dimensional Extended Attribute Grammars(2-D EAGs) for the recognition of hand-printed Chinese characters is presented.This method uses directly two dimensional information,and provides a scheme for dealing with various kinds of specific cases in a uniform way.In this method,components are drawn in guided and redundant way and reductions are made level by leve just in accordance with the component combination relations of Chinese characters.The method provids also polysemous grammars,coexisting grammars and structure inferrings whih constrain redundant recognition by comparison among similar characters of components and greatly increase the tolerance ability to distortion.