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58
Interdisciplinary application of nonlinear time series methods
 Phys. Reports
, 1998
"... This paper reports on the application to field measurements of time series methods developed on the basis of the theory of deterministic chaos. The major difficulties are pointed out that arise when the data cannot be assumed to be purely deterministic and the potential that remains in this situatio ..."
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Cited by 84 (4 self)
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This paper reports on the application to field measurements of time series methods developed on the basis of the theory of deterministic chaos. The major difficulties are pointed out that arise when the data cannot be assumed to be purely deterministic and the potential that remains in this situation is discussed. For signals with weakly nonlinear structure, the presence of nonlinearity in a general sense has to be inferred statistically. The paper reviews the relevant methods and discusses the implications for deterministic modeling. Most field measurements yield nonstationary time series, which poses a severe problem for their analysis. Recent progress in the detection and understanding of nonstationarity is reported. If a clear signature of approximate determinism is found, the notions of phase space, attractors, invariant manifolds etc. provide a convenient framework for time series analysis. Although the results have to be interpreted with great care, superior performance can be achieved for typical signal processing tasks. In particular, prediction and filtering of signals are discussed, as well as the classification of system states by means of time series recordings.
Modeling acoustics in virtual environments using the uniform theory of diffraction
 ACM Computer Graphics, SIGGRAPH’01 Proceedings
, 2001
"... Realistic modeling of reverberant sound in 3D virtual worlds provides users with important cues for localizing sound sources and understanding spatial properties of the environment. Unfortunately, current geometric acoustic modeling systems do not accurately simulate reverberant sound. Instead, they ..."
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Cited by 72 (10 self)
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Realistic modeling of reverberant sound in 3D virtual worlds provides users with important cues for localizing sound sources and understanding spatial properties of the environment. Unfortunately, current geometric acoustic modeling systems do not accurately simulate reverberant sound. Instead, they model only direct transmission and specular reflection, while diffraction is either ignored or modeled through statistical approximation. However, diffraction is important for correct interpretation of acoustic environments, especially when the direct path between sound source and receiver is occluded. The Uniform Theory of Diffraction (UTD) extends geometrical acoustics with diffraction phenomena: illuminated edges become secondary sources of diffracted rays that in turn may propagate through the environment. In this paper, we propose an efficient way for computing the acoustical effect of diffraction paths using the UTD for deriving secondary diffracted rays and associated diffraction coefficients. Our main contributions are: 1) a beam tracing method for enumerating sequences of diffracting edges efficiently and without aliasing in densely occluded polyhedral environments; 2) a practical approximation to the simulated sound field in which diffraction is considered only in shadow regions; and 3) a realtime auralization system demonstrating that diffraction dramatically improves the quality of spatialized sound in virtual environments.
From Simple Features to Sophisticated Evaluation Functions
 Computers and Games, Proceedings of CG98, LNCS 1558
, 1999
"... . This paper discusses a practical framework for the semi automatic construction of evaluation functions for games. Based on a structured evaluation function representation, a procedure for exploring the feature space is presented that is able to discover new features in a computational feasible w ..."
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Cited by 43 (7 self)
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. This paper discusses a practical framework for the semi automatic construction of evaluation functions for games. Based on a structured evaluation function representation, a procedure for exploring the feature space is presented that is able to discover new features in a computational feasible way. Besides the theoretical aspects, related practical issues such as the generation of training positions, feature selection, and weight fitting in large linear systems are discussed. Finally, we present experimental results for Othello, which demonstrate the potential of the described approach. Keywords: automatic feature construction, GLEM, Othello 1 Introduction Many AI systems use evaluation functions for guiding search tasks. In the context of strategy games they usually map game positions into the real numbers for estimating the winning chance for the player to move. Decades of research has shown how hard a problem evaluation function construction is, even when focusing on particular...
Functional Models for Regression Tree Leaves
, 1997
"... This paper presents a study about functional models for regression tree leaves. We evaluate experimentally several alternatives to the averages commonly used in regression trees. We have implemented a regression tree learner (HTL) that is able to use several alternative models in the tree leaves. We ..."
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Cited by 33 (3 self)
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This paper presents a study about functional models for regression tree leaves. We evaluate experimentally several alternatives to the averages commonly used in regression trees. We have implemented a regression tree learner (HTL) that is able to use several alternative models in the tree leaves. We study the effect on accuracy and the computational cost of these alternatives. The experiments carried out on 11 data sets revealed that it is possible to significantly outperform the "naive" averages of regression trees. Among the four alternative models that we evaluated, kernel regressors were usually the best in terms of accuracy. Our study also indicates that by integrating regression trees with other regression approaches we are able to overcome the limitations of individual methods both in terms of accuracy as well as in computational efficiency. 1 INTRODUCTION In this paper we present an empirical evaluation of alternative regression models for the leaves of decision trees that dea...
A Hierarchy of DataBased ENSO Models
, 2005
"... Global sea surface temperature (SST) evolution is analyzed by constructing predictive models that best describe the dataset’s statistics. These inverse models assume that the system’s variability is driven by spatially coherent, additive noise that is white in time and are constructed in the phase s ..."
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Cited by 30 (15 self)
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Global sea surface temperature (SST) evolution is analyzed by constructing predictive models that best describe the dataset’s statistics. These inverse models assume that the system’s variability is driven by spatially coherent, additive noise that is white in time and are constructed in the phase space of the dataset’s leading empirical orthogonal functions. Multiple linear regression has been widely used to obtain inverse stochastic models; it is generalized here in two ways. First, the dynamics is allowed to be nonlinear by using polynomial regression. Second, a multilevel extension of classic regression allows the additive noise to be correlated in time; to do so, the residual stochastic forcing at a given level is modeled as a function of variables at this level and the preceding ones. The number of variables, as well as the order of nonlinearity, is determined by optimizing model performance. The twolevel linear and quadratic models have a better El Niño–Southern Oscillation (ENSO) hindcast skill than their onelevel counterparts. Estimates of skewness and kurtosis of the models ’ simulated Niño3 index reveal that the quadratic model reproduces better the observed asymmetry between the positive El Niño and negative La Niña events. The benefits of the quadratic model are less clear in terms of its overall, crossvalidated hindcast skill; this model outperforms, however, the linear one in predicting the magnitude of extreme SST anomalies. Seasonal ENSO dependence is captured by incorporating additive, as well as multiplicative forcing with a 12month period into the first level of each model. The quasiquadrennial ENSO oscillatory mode is robustly simulated by all models. The “spring barrier ” of ENSO forecast skill is explained by Floquet and singular vector analysis, which show that the leading ENSO mode becomes strongly damped in summer, while nonnormal optimum growth has a strong peak in December.
SnakeToonz: A SemiAutomatic Approach to Creating Cel Animation from Video
, 2002
"... SnakeToonz is an interactive system that allows children and others untrained in cel animation to create twodimensional cartoons from video streams and images. The ability to create cartoons has traditionally been limited to professional animation houses and trained artists. SnakeToonz aims to give ..."
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Cited by 27 (1 self)
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SnakeToonz is an interactive system that allows children and others untrained in cel animation to create twodimensional cartoons from video streams and images. The ability to create cartoons has traditionally been limited to professional animation houses and trained artists. SnakeToonz aims to give anyone with a video camera and a computer the ability to create compelling cel animation. This is done by combining constraints of the cartooning medium with simple user input and analysis of that input.
Reconstruction of multidimensional signals from irregular noisy samples
 IEEE TRANS. SIGNAL PROCESS
, 2008
"... We focus on a multidimensional field with uncorrelated spectrum and study the quality of the reconstructed signal when the field samples are irregularly spaced and affected by independent and identically distributed noise. More specifically, we apply linear reconstruction techniques and take the me ..."
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Cited by 12 (4 self)
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We focus on a multidimensional field with uncorrelated spectrum and study the quality of the reconstructed signal when the field samples are irregularly spaced and affected by independent and identically distributed noise. More specifically, we apply linear reconstruction techniques and take the meansquare error (MSE) of the field estimate as a metric to evaluate the signal reconstruction quality. We find that the MSE analysis could be carried out by using the closedform expression of the eigenvalue distribution of the matrix representing the sampling system. Unfortunately, such distribution is still unknown. Thus, we first derive a closedform expression of the distribution moments, and we find that the eigenvalue distribution tends to the Marčenko–Pastur distribution as the field dimension goes to infinity. Finally, by using our approach, we derive a tight approximation to the MSE of the reconstructed field.
MultiImage Focus of Attention for Rapid Site Model Construction
 IEEE Int. Conf. on Computer Vision and Pattern Recognition
, 1997
"... A multiimage focus of attention mechanism has been developed that can quickly distinguish raised objects like buildings from structured background clutter typical to many aerial image scenarios. The underlying approach is the spacesweep stereo method, in which features from multiple images are bac ..."
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Cited by 10 (0 self)
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A multiimage focus of attention mechanism has been developed that can quickly distinguish raised objects like buildings from structured background clutter typical to many aerial image scenarios. The underlying approach is the spacesweep stereo method, in which features from multiple images are backprojected onto a virtual, horizontal plane that is methodically swept through the scene. Backprojected gradient orientations from multiple images are highly correlated when they come from scene locations containing structural edges that are roughly horizontal, like building roofs and terrain; otherwise, they tend to be uniformly distributed. These observations are used to define a structural salience measure that can determine whether a given volume of space contains a statistically significant number of structural edges, without first performing precise reconstruction of those edges. The utility of structural salience for computing focus of attention regions is illustrated on sample data f...
Mindmap: Utilizing multiple taxonomies and visualization to understand a document collection
 In Proceedings of the Hawaii International Conference on System Sciences
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Relaxed Conditions for Sparse Signal Recovery with General Concave Priors
"... Sensing challenges the convention of modern digital signal processing by establishing that exact signal reconstruction is possible for many problems where the sampling rate falls well below the Nyquist limit. Following the landmark works of Candès et al. and Donoho on the performance of ℓ1minimizat ..."
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Cited by 7 (0 self)
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Sensing challenges the convention of modern digital signal processing by establishing that exact signal reconstruction is possible for many problems where the sampling rate falls well below the Nyquist limit. Following the landmark works of Candès et al. and Donoho on the performance of ℓ1minimization models for signal reconstruction, several authors demonstrated that certain nonconvex reconstruction models consistently outperform the convex ℓ1model in practice at very low sampling rates despite the fact that no global minimum can be theoretically guaranteed. Nevertheless, there has been little theoretical investigation into the performance of these nonconvex models. In this work, a notion of weak signal recoverability is introduced and the performance of nonconvex reconstruction models employing general concave metric priors is investigated under this model. The sufficient conditions for establishing weak signal recoverability are shown to substantially relax as the prior functional is parameterized to more closely resemble the targeted ℓ0model, offering new insight into the empirical performance of this general class of reconstruction methods. Examples of relaxation trends are shown for several different prior models.