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.

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Design techniques for static information are well understood, their descriptions and discourse thorough and well-evolved. 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.

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. ...

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In this chapter, I review the main methods and techniques of complex systems science. As a

We present a physically-based, yet fast and simple method to simulate gaseous phenomena. In our approach, the incompressible Navier-Stokes (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 real-time, an accurate velocity field and can incorporate an optional temperature field to account for the buoyancy force of hot gas. Because

Digital sound synthesizers, ubiquitous today in sound cards, software and dedicated hardware, use algorithms (Sound Synthesis Techniques, SSTs) capable of generating sounds similar to those of acoustic instruments and even totally novel sounds. The design of SSTs is a very hard problem. It is usually assumed that it requires human ingenuity to design an algorithm suitable for synthesizing a sound with certain characteristics. Many of the SSTs commonly used are the fruit of experimentation and a long refinement processes. A SST is determined by its “functional form ” and “internal parameters”. Design of SSTs is usually done by selecting a fixed functional form from a handful of commonly used SSTs, and performing a parameter estimation technique to find a set of internal parameters that will best emulate the target sound. A new approach for automating the design of SSTs is proposed. It uses a set of examples of the desired behavior of the SST in the form of “inputs + target sound”. The approach is capable of suggesting novel functional forms and their internal parameters, suited to follow closely the given examples.

A multi-user, polyphonic sensor stage environment that maps position and gestures of up to four performers to the pitch and articulation of distinct notes is presented. The design seeks to provide multiple players on a stage with the feeling of a traditional acoustic instrument by giving them complete control over the instrument's expressive parameters and a clear causal connection between their actions and the resulting sound. The positions of the performers are determined by a custom ultrasonic tracking system, while hand motions are measured by custom-made gloves containing accelerometer units. Furthermore, juggling clubs are illuminated dynamically to make complex juggling patterns more apparent. The system is currently on tour with the Flying Karamazov Brothers juggling troupe. # Present address: ThingMagic LLC, Cambridge, MA 02142 # Present address: Trimble Navigation Ltd, Sunnyvale, CA 94088 CR Categories: B.4.2 [Input/output and data communications]: input/output devices---Channels and controllers; J.5 [Arts and Humanities ]: Performing Arts Keywords: Applications, HCI (Human-Computer Interface), Object Tracking, Spatialized Sound, User Interface Design 1

We present a framework for the analysis and synthesis of acoustical instruments based on data-driven probabilistic inference modeling. Audio time series and boundary conditions of a played instrument are recorded and the non-linear mapping from the control data into the audio space is inferred using the general inference framework of Cluster-Weighted Modeling. The resulting model is used for real-time synthesis of audio sequences from new input data. I. INTRODUCTION Sampling of acoustical instruments [Massie, 1998] and synthesis based on detailed firstprinciples physical modeling [Smith, 1992] have been two particularly successful musical synthesis techniques. The sampling approach typically results in high sound quality, but has no notion of the instrument as a dynamic system with variable control. The physical modeling approach retains this dynamic control but results in intractably large models when all the physical degrees of freedom are considered. The search for the right combina...