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82
An algorithmic introduction to numerical simulation of stochastic differential equations
 SIAM Review
, 2001
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Properties of embedding methods for similarity searching in metric spaces
 PAMI
, 2003
"... Complex data types—such as images, documents, DNA sequences, etc.—are becoming increasingly important in modern database applications. A typical query in many of these applications seeks to find objects that are similar to some target object, where (dis)similarity is defined by some distance functi ..."
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Cited by 80 (4 self)
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Complex data types—such as images, documents, DNA sequences, etc.—are becoming increasingly important in modern database applications. A typical query in many of these applications seeks to find objects that are similar to some target object, where (dis)similarity is defined by some distance function. Often, the cost of evaluating the distance between two objects is very high. Thus, the number of distance evaluations should be kept at a minimum, while (ideally) maintaining the quality of the result. One way to approach this goal is to embed the data objects in a vector space so that the distances of the embedded objects approximates the actual distances. Thus, queries can be performed (for the most part) on the embedded objects. In this paper, we are especially interested in examining the issue of whether or not the embedding methods will ensure that no relevant objects are left out (i.e., there are no false dismissals and, hence, the correct result is reported). Particular attention is paid to the SparseMap, FastMap, and MetricMap embedding methods. SparseMap is a variant of Lipschitz embeddings, while FastMap and MetricMap are inspired by dimension reduction methods for Euclidean spaces (using KLT or the related PCA and SVD). We show that, in general, none of these embedding methods guarantee that queries on the embedded objects have no false dismissals, while also demonstrating the limited cases in which the guarantee does hold. Moreover, we describe a variant of SparseMap that allows queries with no false dismissals. In addition, we show that with FastMap and MetricMap, the distances of the embedded objects can be much greater than the actual distances. This makes it impossible (or at least impractical) to modify FastMap and MetricMap to guarantee no false dismissals.
Organic information design
, 2000
"... Design techniques for static information are well understood, their descriptions and discourse thorough and wellevolved. But these techniques fail when dynamic information is considered. There is a space of highly complex systems for which we lack deep understanding because few techniques exist for ..."
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Cited by 28 (0 self)
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Design techniques for static information are well understood, their descriptions and discourse thorough and wellevolved. But these techniques fail when dynamic information is considered. There is a space of highly complex systems for which we lack deep understanding because few techniques exist for visualization of data whose structure and content are continually changing. To approach these problems, this thesis introduces a visualization process titled Organic Information Design. The resulting systems employ simulated organic properties in an interactive, visually refined environment to glean qualitative facts from large bodies of quantitative data generated by dynamic information sources.
Creating and Exploring a Large Photorealistic Virtual Space
"... The supplementary video can be viewed at: ..."
A Logical Account of Causal and Topological Maps
, 2001
"... The Spatial Semantic Hierarchy (SSH) is a set of distinct representations for large scale space, each with its own ontology and each abstracted from the levels below it. At the control level, the agent and its environment are modeled as continuous dynamical systems whose equilibrium points are abstr ..."
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Cited by 15 (2 self)
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The Spatial Semantic Hierarchy (SSH) is a set of distinct representations for large scale space, each with its own ontology and each abstracted from the levels below it. At the control level, the agent and its environment are modeled as continuous dynamical systems whose equilibrium points are abstracted to a discrete set of distinctive states. The control laws whose execution defines trajectories linking these states are abstracted to actions, giving a discrete causal graph representation for the state space. The causal graph of states and actions is in turn abstracted to a topological network of places and paths (i.e. the topological map). Local metrical models of places and paths can be built within the framework of the control, causal and topological levels while avoiding problems of global consistency. ...
Evaluation of Kalman Filtering for Network Time Keeping
 IEEE Transactions on Ultrasonics, Ferromagnetics and Frequency Control
"... Time information is critical for a variety of applications in distributed environments that facilitate pervasive computing and communication. This work describes and evaluates a novel Kalman filtering algorithm for endtoend time synchronization between a client computer and a server of “true ” tim ..."
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Cited by 15 (11 self)
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Time information is critical for a variety of applications in distributed environments that facilitate pervasive computing and communication. This work describes and evaluates a novel Kalman filtering algorithm for endtoend time synchronization between a client computer and a server of “true ” time (e.g. a GPS source) using messages transmitted over packet switched networks, such as the Internet. The messages exchanged have the NTP format and the algorithm evaluated, is performed only at the client side. The Kalman filtering algorithm is compared to two other techniques widely used, based on linear programming and statistical averaging and the experiments involve independent consecutive measurements (Gaussian case) or measurements exhibiting longrange dependence (Selfsimilar case). Performance is evaluated according to the estimation error of frequency offset and time offset between client and server clock, the standard deviation of the estimates and the number of packets used for a specific estimation. The algorithms could exploit existing NTP infrastructure and a specific example is presented.
Driven by information: a tectonic theory of Stroop effects
 Psychological Review
, 2003
"... The goal of avoiding distraction (e.g., ignoring words when naming their print colors in a Stroop task) is opposed intrinsically by the penchant to process conspicuous and correlated characteristics of the environment (e.g., noticing trialtotrial associations between the colors and the words). To ..."
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Cited by 15 (2 self)
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The goal of avoiding distraction (e.g., ignoring words when naming their print colors in a Stroop task) is opposed intrinsically by the penchant to process conspicuous and correlated characteristics of the environment (e.g., noticing trialtotrial associations between the colors and the words). To reconcile these opposing forces, the authors propose a tectonic theory of selective attention in which 2 memorybased structures—dimensional imbalance and dimensional uncertainty—drive selection by processing salient, surprising, and/or correlated information contained within and across stimulus dimensions. Each structure modulates the buildup of excitation to targets and the buildup of inhibition to distractors and to memories of previous stimuli. Tectonic theory is implemented to simulate the impact of 4 types of context on the presence, magnitude, and direction of congruity effects and task effects in the Stroop paradigm. The tectonic model is shown to surpass other formal models in explaining the range and diversity of Stroop effects. Humans are prodigious at focusing on selected aspects of their environment. They can attend to a melody played by the string section of a symphonic orchestra apart from another melody played concurrently by the woodwinds. They can concentrate on
The LatticeBoltzmann Method for Simulating Gaseous Phenomena
 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
, 2004
"... We present a physicallybased, yet fast and simple method to simulate gaseous phenomena. In our approach, the incompressible NavierStokes (NS) equations governing fluid motion have been modeled in a novel way to achieve a realistic animation. We introduce the Lattice Boltzmann Model (LBM), which ..."
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Cited by 14 (1 self)
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We present a physicallybased, yet fast and simple method to simulate gaseous phenomena. In our approach, the incompressible NavierStokes (NS) equations governing fluid motion have been modeled in a novel way to achieve a realistic animation. We introduce the Lattice Boltzmann Model (LBM), which simulates the microscopic movement of fluid particles by linear and local rules on a grid of cells so that the macroscopic averaged properties obey the desired NS equations. The LBM is defined on a 2D or 3D discrete lattice, which is used to solve fluid animation based on different boundary conditions. The LBM simulation generates, in realtime, an accurate velocity field and can incorporate an optional temperature field to account for the buoyancy force of hot gas. Because
Methods and techniques of complex systems science: An overview
, 2003
"... In this chapter, I review the main methods and techniques of complex systems science. As a ..."
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Cited by 11 (0 self)
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In this chapter, I review the main methods and techniques of complex systems science. As a
NONLINEAR DYNAMICAL SYSTEM IDENTIFICATION FROM UNCERTAIN AND INDIRECT MEASUREMENTS
, 2002
"... We review the problem of estimating parameters and unobserved trajectory components from noisy time series measurements of continuous nonlinear dynamical systems. It is first shown that in parameter estimation techniques that do not take the measurement errors explicitly into account, like regressio ..."
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Cited by 10 (0 self)
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We review the problem of estimating parameters and unobserved trajectory components from noisy time series measurements of continuous nonlinear dynamical systems. It is first shown that in parameter estimation techniques that do not take the measurement errors explicitly into account, like regression approaches, noisy measurements can produce inaccurate parameter estimates. Another problem is that for chaotic systems the cost functions that have to be minimized to estimate states and parameters are so complex that common optimization routines may fail. We show that the inclusion of information about the timecontinuous nature of the underlying trajectories can improve parameter estimation considerably. Two approaches, which take into account both the errorsinvariables problem and the problem of complex cost functions, are described in detail: shooting approaches and recursive estimation techniques. Both are demonstrated on numerical examples.