This research-to-practice WIP paper describes an approach to engage high school students in research through the utilization of citizen science tools embedded with Machine Learning (ML) models. In the context of fostering early engagement in scientific research among high school students, this paper explores the integration of citizen science and ML using SmartCS, an existing platform for creating citizen science smartphone applications. The process requires no prior programming knowledge, making it accessible to a broad range of students. For our approach, a group of high school students participated in a two-month-long summer research program, where they were introduced to the principles of citizen science as a method for data collection across diverse scientific projects from different research domains. The program's initial task involved students in the conceptualization of a citizen science project, adopted based on a thorough literature review, followed by the practical task of developing a smartphone application for data collection and educational purposes. Students either created new datasets or curated existing ones to train lightweight ML models for computer vision tasks, specifically focused on providing visual guidance within these mobile apps. The final task involved deploying these applications for public use and collecting user feedback. Our experience suggests that this approach not only enabled students to learn aspects of computer science and engineering, particularly in the area of ML model training and mobile application software development, but also allowed them to experience firsthand the significant role citizen science can play in collecting and analyzing scientific data.

We present a hybrid machine learning and flow analysis feature detection method, RipViz, to extract rip currents from stationary videos. Rip currents are dangerous strong currents that can drag beachgoers out to sea. Most people are either unaware of them or do not know what they look like. In some instances, even trained personnel such as lifeguards have difficulty identifying them. RipViz produces a simple, easy to understand visualization of rip location overlaid on the source video. With RipViz, we first obtain an unsteady 2D vector field from the stationary video using optical flow. Movement at each pixel is analyzed over time. At each seed point, sequences of short pathlines, rather a single long pathline, are traced across the frames of the video to better capture the quasi-periodic flow behavior of wave activity. Because of the motion on the beach, the surf zone, and the surrounding areas, these pathlines may still appear very cluttered and incomprehensible. Furthermore, lay audiences are not familiar with pathlines and may not know how to interpret them. To address this, we treat rip currents as a flow anomaly in an otherwise normal flow. To learn about the normal flow behavior, we train an LSTM autoencoder with pathline sequences from normal ocean, foreground, and background movements. During test time, we use the trained LSTM autoencoder to detect anomalous pathlines (i.e., those in the rip zone). The origination points of such anomalous pathlines, over the course of the video, are then presented as points within the rip zone. RipViz is fully automated and does not require user input. Feedback from domain expert suggests that RipViz has the potential for wider use.

Face models are widely used in image processing and other domains. The input data to create a 3D face model ranges from accurate laser scans to simple 2D RGB photographs. These input data types are typically deficient either due to missing regions, or because they are underconstrained. As a result, reconstruction methods include embedded priors encoding the valid domain of faces. System designers must choose a source of input data and then choose a reconstruction method to obtain a usable 3D face. If a particular application domain requires accuracy X, which kinds of input data are suitable? Does the input data need to be 3D, or will 2D data suffice? This paper takes a step toward answering these questions using synthetic data. A ground truth dataset is used to analyze accuracy obtainable from 2D landmarks, 3D landmarks, low quality 3D, high quality 3D, texture color, normals, dense 2D image data, and when regions of the face are missing. Since the data is synthetic it can be analyzed both with and without measurement error. This idealized synthetic analysis is then compared to real results from several methods for constructing 3D faces from 2D photographs. The experimental results suggest that accuracy is severely limited when only 2D raw input data exists.

The formation of cosmic structure results from the action of gravity on matter in an expanding Universe. As the evolution proceeds, the velocity field changes from being single-valued almost everywhere in space to being multi-valued over a complex web of `multistreaming' regions associated with the formation of large-scale structure (LSS) such as halos (or clumps), filaments, and sheets. Until recently, these structures have been investigated primarily via the (scalar) mass density field. In this application paper we apply data analysis and visualization techniques to cosmological simulations with the aim of studying multistreaming regions using velocity-based probes. Compared to the current practice of using density information (e.g., morphology estimators, locating overdense regions with halo finders), we show that velocity-based methods can provide useful supporting, as well as complementary, information. Because the density field and multistreaming are correlated but do not contain the same information, new and interesting information about the properties of the large-scale structure may be extracted, e.g., capturing dynamical behavior not possible with density-based estimators. Incorporating a novel method for setting thresholds for the velocity-based estimators, we study the relationships between the density field as represented by compact overdense halos and the different properties of multistreaming regions as represented by different velocity-based estimators.

Feature Flow Fields are a well-accepted approach for extracting and tracking features. In particular, they are often used to track critical points in time-dependent vector fields and to extract and track vortex core lines. The general idea is to extract the feature or its temporal evolution using a stream line integration in a derived vector field-the so-called Feature Flow Field (FFF). Hence, the desired feature line is a stream line of the FFF. As we will carefully analyze in this paper, the stream lines around this feature line may diverge from it. This creates an unstable situation: if the integration moves slightly off the feature line due to numerical errors, then it will be captured by the diverging neighborhood and carried away from the real feature line. The goal of this paper is to define a new FFF with the guarantee that the neighborhood of a feature line has always converging behavior. This way, we have an automatic correction of numerical errors: if the integration moves slightly off the feature line, it automatically moves back to it during the ongoing integration. This yields results which are an order of magnitude more accurate than the results from previous schemes. We present new stable FFF formulations for the main applications of tracking critical points and solving the Parallel Vectors operator. We apply our method to a number of data sets.

A new method for finding the locus of parallel vectors is presented, called PVsolve. A parallel-vector operator has been proposed as a visualization primitive, as several features can be expressed as the locus of points where two vector fields are parallel. Several applications of the idea have been reported, so accurate and efficient location of such points is an important problem. Previously published methods derive a tangent direction under the assumption that the two vector fields are parallel at the current point in space, then extend in that direction to a new point. PVsolve includes additional terms to allow for the fact that the two vector fields may not be parallel at the current point, and uses a root-finding approach. Mathematical analysis sheds new light on the feature flow field technique (FFF) as well. The root-finding property allows PVsolve to use larger step sizes for tracing parallel-vector curves, compared to previous methods, and does not rely on sophisticated differential equation techniques for accuracy. Experiments are reported on fluid flow simulations, comparing FFF and PVsolve.

The study of stress and strains in soils and structures (solids) help us gain a better understanding of events such as failure of bridges, dams and buildings, or accumulated stresses and strains in geological subduction zones that could trigger earthquakes and subsequently tsunamis. In such domains, the key feature of interest is the location and orientation of maximal shearing planes. This paper describes a method that highlights this feature in stress tensor fields. It uses a plane-in-a-box glyph which provides a global perspective of shearing planes based on local analysis of tensors. The analysis can be performed over the entire domain, or the user can interactively specify where to introduce these glyphs. Alternatively, they can also be placed depending on the threshold level of several physical relevant parameters such as double couple and compensated linear vector dipole. Both methods are tested on stress tensor fields from geomechanics.

This paper presents a strategy for seeding streamlines in 3D flow fields. Its main goal is to capture the essential flow patterns and to provide sufficient coverage in the field while reducing clutter. First, critical points of the flow field are extracted to identify regions with important flow patterns that need to be presented. Different seeding templates are then used around the vicinity of the different critical points. Because there is significant variability in the flow pattern even for the same type of critical point, our template can change shape depending on how far the critical point is from transitioning into another type of critical point. To accomplish this, we introduce the /spl alpha/-/spl beta/ map of 3D critical points. Next, we use Poisson seeding to populate the empty regions. Finally, we filter the streamlines based on their geometric and spatial properties. Altogether, this multi-step strategy reduces clutter and yet captures the important 3D flow features.

Analysis of degenerate tensors is a fundamental step in finding the topological structures and separatrices in tensor fields. Previous work in this area have been limited to analyzing symmetric second order tensor fields. In this paper, we extend the topological analysis to 2D general (asymmetric) second order tensor fields. We show that it is not sufficient to define degeneracies based on eigenvalues alone, but one must also include the eigenvectors in the analysis. We also study the behavior of these eigenvectors as they cross from one topological region into another.

Tensor topology is useful in providing a simplified and yet detailed representation of a tensor field. Recently the field of 3D tensor topology is advanced by the discovery that degenerate tensors usually form lines in their most basic configurations. These lines form the backbone for further topological analysis. A number of ways for extracting and tracing the degenerate tensor lines have also been proposed. In this paper, we complete the previous work by studying the behavior and extracting the separating surfaces emanating from these degenerate lines. First, we show that analysis of eigenvectors around a 3D degenerate tensor can be reduced to 2D. That is, in most instances, the 3D separating surfaces are just the trajectory of the individual 2D separatrices which includes trisectors and wedges. But the proof is by no means trivial since it is closely related to perturbation theory around a pair of singular slate. Such analysis naturally breaks down at the tangential points where the degenerate lines pass through the plane spanned by the eigenvectors associated with the repeated eigenvalues. Second, we show that the separatrices along a degenerate line may switch types (e.g. trisectors to wedges) exactly at the points where the eigenplane is tangential to the degenerate curve. This property leads to interesting and yet complicated configuration of surfaces around such transition points. Finally, we apply the technique to several common data sets to verify its correctness.

This paper addresses several issues related to topological analysis of 3D second order symmetric tensor fields. First, we show that the degenerate features in such data sets form stable topological lines rather than points, as previously thought. Second, the paper presents two different methods for extracting these features by identifying the individual points on these lines and connecting them. Third, this paper proposes an analytical form of obtaining tangents at the degenerate points along these topological lines. The tangents are derived from a Hessian factorization technique on the tensor discriminant and leads to a fast and stable solution. Together, these three advances allow us to extract the backbone topological lines that form the basis for topological analysis of tensor fields.

Visualization of 3D tensor fields continues to be a major challenge in terms of providing intuitive and uncluttered images that allow the users to better understand their data. The primary focus of this paper is on finding a formulation that lends itself to a stable numerical algorithm for extracting stable and persistent topological features from 2nd order real symmetric 3D tensors. While features in 2D tensors can be identified as either wedge or trisector points, in 3D, the corresponding stable features are lines, not just points. These topological feature lines provide a compact representation of the 3D tensor field and are essential in helping scientists and engineers understand their complex nature. Existing techniques work by finding degenerate points and are not numerically stable, and worse, produce both false positive and false negative feature points. This work seeks to address this problem with a robust algorithm that can extract these features in a numerically stable, accurate, and complete manner.

There are many situations where one needs to compare two or more data sets. It may be to compare different models, different resolutions, differences in algorithms, different experimental results, etc. There is therefore a need for comparative visualization tools to help analyze the differences. This paper focuses on comparative visualization tools for analyzing flow or vector data sets. The techniques presented allow one to compare individual streamlines and streamribbons as well as a dense field of streamlines. These comparison methods can also be used to study differences in vortex cores that are represented as polylines.

The use of remotely sensed images to map continuous biophysical variables, such as those related to terrestrial vegetation amount, sea surface temperature, and many other targets of NASA's Earth Observing System (EOS), includes variable, parametric, positional, spatial support and structural sources of uncertainty. A complete description of uncertainty will lead to a probability distribution at each location, allowing the exploration of the spatial dimension of uncertainty, that is, where the field is not well quantified. To achieve this purpose, convenient visualization tools are required. We have produced such a tool, called PDFVis, that facilitates the display of probability density functions (pdfs) on a per-grid-cell basis. The density estimate from Monte-Carlo generated realizations is interactively displayed as well as parametric and non-parametric summaries of the pdf field (such as mean, median, quartiles, standard deviation, number of modes, and locations of modes.) Shaded surface renderings of pdfs along a transect can also be projected onto a plane. This tool will become more useful as richer descriptions of spatial uncertainty become available.

We describe our experience graphically visualizing data access behavior, with a specific emphasis on visualizing the predictability of such accesses and the consistency of these observations at the block level. Such workloads are more frequently encountered after filtering through intervening cache levels and in this paper we demonstrate how such filtered workloads pose a problem for traditional caching schemes. We demonstrate how prior results are consistent across both file and disk access workloads. We also demonstrate how an aggregating cache based on predictive grouping can overcome such filtering effects. Our visualization tool provides an illustration of how file workloads remain predictable in the presence of intervening caches, explaining how the aggregating cache can remain effective under what would normally be considered adverse conditions. We further demonstrate how the same predictability remains true with physical block workloads.

We introduce a new method for visualizing symmetric tensor fields. The technique produces images and animations reminiscent of line integral convolution (LIC). The technique is also slightly related to hyperstreamlines in that it is used to visualize tensor fields. However, the similarity ends there. HyperLIC uses a multi-pass approach to show the anisotropic properties in a 2D or 3D tensor field. We demonstrate this technique using data sets from computational fluid dynamics as well as diffusion-tensor MRI.

Remote sensing analyses usually result in maps of discrete or continuous variables. Ideally, each value in such a map should be accompanied by an uncertainty description, that is, a quantitative statement about the probability of error. A full description of uncertainty at each pixel is usefully represented using a probability distribution. Such a probability distribution may be based on an understanding of potential errors in position, spatial support (the area measured by the sensor's field of view), model parameters, the model structure, and the input variables to the model. Visualizing uncertainty in the products of remote sensing analysis presents the challenge that at least four dimensions are required. These are the spatial dimensions (x and y), the dimension of the variable being mapped and finally the probability dimension. Current visualization tools and techniques do not support these data sets directly. Even animation, a logical choice to represent other four dimensional problems, is not fully satisfactory for probability distribution data sets. We have first addressed the problem of visualizing uncertainty by creating interactive maps of first, second and third order statistics summarizing the distributions. Next, we have experimented with shaded surface rendering of distributions from a user-selectable profile (row or column) in the image. We demonstrate these methods using a data set generated by a geostatistical conditional simulation algorithm and a single band image and we discuss the future promise of visualizing all four dimensions at once.

Box plot is a compact representation that encodes the minimum, maximum, mean, median, and quartile information of a distribution. In practice, a single box plot is drawn for each variable of interest. With the advent of more accessible computing power, we are now facing the problem of visualizing data where there is a distribution at each 2D spatial location. Simply extending the box plot technique to distributions over 2D domain is not straightforward. One challenge is reducing the visual clutter if a box plot is drawn over each grid location in the 2D domain. This paper presents and discusses two general approaches, using parametric statistics and shape descriptors, to present 2D distribution data sets. Both approaches provide additional insights compared to the traditional box plot technique.

Visualizing second-order 3D tensor fields continue to be a challenging task. Although there are several algorithms that have been presented, no single algorithm by itself is sufficient for the analysis because of the complex nature of tensor fields. In this paper, we present two new methods, based on volume deformation, to show the effects of the tensor field upon its underlying media. We focus on providing a continuous representation of the nature of the tensor fields. Each of these visualization algorithms is good at displaying some particular properties of the tensor field.

The paper proposes the use of specially generated 3D procedural textures for visualizing steady state 2D flow fields. We use the flow field to advect and animate the texture over time. However, using standard texture advection techniques and arbitrary textures will introduce some undesirable effects such as: (a) expanding texture from a critical source point, (b) streaking pattern from the boundary of the flow field, (c) crowding of advected textures near an attracting spiral or sink, and (d) absent or lack of textures in some regions of the flow. The paper proposes a number of strategies to solve these problems. We demonstrate how the technique works using both synthetic data and computational fluid dynamics data.

This paper introduces a new relatively inexpensive technique - stream bubbles for 3D flow visualization. The physical analogy to this technique are bubbles that can be observed in nature with different shapes and varying speeds. A stream bubble is a surface, defined by a small set of vertices, advected through a flow field. It can easily manifest flow features like twist, stretch, expansion and rotation. Upon encountering an obstacle, stream bubbles will automatically erode and/or split in an intuitively geometric way. For highly divergent and vortical fields, it can also break apart based on the aspect ratio of the bounding volume. When two or more stream bubbles meet, no explicit merge operation is needed since the surfaces will simply intersect each other to form a composite surface. No effort is made to make the intersection smooth. Stream bubbles may be of different sizes. Larger bubbles give a coarse global view of the flow structure, while smaller ones give a more accurate depiction. In addition, our interface provides an interactive, multi-resolution visualization environment facilitated with an animated or step-by-step playback function.

Maps of biophysical and geophysical variables using Earth Observing System (EOS) satellite image data are an important component of Earth science. These maps have a single value derived at every grid cell and standard techniques are used to visualize them. Current tools fall short, however, when it is necessary to describe a distribution of values at each grid cell. Distributions may represent a frequency of occurrence over time, frequency of occurrence from multiple runs of an ensemble forecast or possible values from an uncertainty model. We identify these "distribution data sets" and present a case study to visualize such 2D distributions. Distribution data sets are different from multivariate data sets in the sense that the values are for a single variable instead of multiple variables. Data for this case study consists of multiple realizations of percent forest cover, generated using a geostatistical technique that combines ground measurements and satellite imagery to model uncertainty about forest cover. We present two general approaches for analyzing and visualizing such data sets. The first is a pixel-wise analysis of the probability density functions for the 2D image while the second is an analysis of features identified within the image. Such pixel-wise and feature-wise views will give Earth scientists a more complete understanding of distribution data sets. See www.cse.ucsc.edu/research/avis/nasa is for additional information.

Direct volume rendering (DVR) algorithms do not generate intermediate geometry to create a visualization, yet they produce countless variations in the resulting images. Therefore, comparative studies are essential for objective interpretation. Even though image and data level comparison metrics are available, it is still difficult to compare results because of the numerous rendering parameters and algorithm specifications involved. Most of the previous comparison methods use information from the final rendered images only. We overcome limitations of image level comparisons with our data level approach using intermediate rendering information. We provide a list of rendering parameters and algorithm specifications to guide comparison studies. We extend Williams and Uselton's rendering parameter list with algorithm specification items and provide guidance on how to compare algorithms. Real data are often too complex to study algorithm variations with confidence. Most of the analytic test data sets reported are often useful only for a limited feature of DVR algorithms. We provide simple and easily reproducible test data sets, a checkerboard and a ramp, that can make clear differences in a wide range of algorithm variations. With data level metrics, our test data sets make it possible to perform detailed comparison studies. A number of examples illustrate how to use these tools.

The paper presents a seed placement strategy for streamlines based on flow features in the dataset. The primary goal of our seeding strategy is to capture flow patterns in the vicinity of critical points in the flow field, even as the density of streamlines is reduced. Secondary goals are to place streamlines such that there is sufficient coverage in non-critical regions, and to vary the streamline placements and lengths so that the overall presentation is aesthetically pleasing (avoid clustering of streamlines, avoid sharp discontinuities across several streamlines, etc.). The procedure is straightforward and non-iterative. First, critical points are identified. Next, the flow field is segmented into regions, each containing a single critical point. The critical point in each region is then seeded with a template depending on the type of critical point. Finally, additional seed points are randomly distributed around the field using a Poisson disk distribution to minimize closely spaced seed points. The main advantage of this approach is that it does not miss the features around critical points. Since the strategy is not image-guided, and hence not view dependent, significant savings are possible when examining flow fields from different viewpoints, especially for 3D flow fields.

Currently, there is tremendous interest in providing ad-hoc mining capabilities in database management systems. As a first step towards this goal, in [15] we proposed an architecture for supporting constraint-based, human-centered, exploratory mining of various kinds of rules including associations, introduced the notion of constrained frequent set queries (CFQs), and developed effective pruning optimizations for CFQs with 1-variable (1-var) constraints.

Much of the research in scientific visualization has focused on complete sets of gridded data. The paper presents our experience dealing with gridded data sets with a large number of missing or invalid data, and some of our experiments in addressing the shortcomings of standard off-the-shelf visualization algorithms. In particular, we discuss the options in modifying known algorithms to adjust to the specifics of sparse datasets, and provide a new technique to smooth out the side-effects of the operations. We apply our findings to data acquired from NEXRAD (NEXt generation RADars) weather radars, which usually have no more than 3 to 4 percent of all possible cell points filled.

This paper explores mapping strategies for generating LIC-like images from streamlines and streamline-like images from LIC. The main contribution of this paper is a technique which we call pseudo-LIC or PLIC. By adjusting a small set of key parameters, PLIC can generate flow visualizations that span the spectrum of streamline-like to LIC-like images. Among the advantages of PLIC are: image quality comparable with LIC, performance speedup over LIC, use of a template texture that is independent of the size of the flow field, handles the problem of multiple streamlines occupying the same pixel in image space, reduced aliasing, applicability to time varying data sets, and variable speed animation.

The authors present a new method for interactive visualization of distortion in map projections. The central idea is a floating ring on a sphere (globe) that can be interactively positioned and scaled. As the ring is manipulated on the globe, the corresponding projection of the ring is distorted using the same map projection parameters. They apply this method to study a real and angular distortion. This method is particularly useful when analyzing large geographical extents (such as in global climate studies) where distortions are significant, as well as visualizations for which information is geo-referenced and perhaps scaled to the underlying map. It serves as a reminder that distortion exists in maps and provides information about the degree, location, and type of distortion.

From the standpoint of supporting human-centered discovery of knowledge, the present-day model of mining association rules suffers from the following serious shortcomings: (i) lack of user exploration and control, (ii) lack of focus, and (iii) rigid notion of relationships. In effect, this model functions as a black-box, admitting little user interaction in between. We propose, in this paper, an architecture that opens up the black-box, and supports constraint-based, human-centered exploratory mining of associations. The foundation of this architecture is a rich set of constraint constructs, including domain, class, and SQL-style aggregate constraints, which enable users to clearly specify what associations are to be mined. We propose constrained association queries as a means of specifying the constraints to be satisfied by the antecedent and consequent of a mined association.

This paper presents techniques for interactively visualizing tensor fields using deformations. The conceptual idea behind this approach is to allow the tensor field to manifest its influence on idealized objects placed within the tensor field. This is similar, though not exactly the same, to surfaces deforming under load in order to relieve built up stress and strain. We illustrate the effectiveness of the Deviator-Isotropic tensor decomposition in deformation visualizations of CFD strain rate. We also investigate how directional flow techniques can be extended to distinguish between regions of tensile versus compressive forces.

The paper describes the architecture of a data level comparative visualization system and experiences using it to study computational fluid dynamics data and experimental wind tunnel data. We illustrate how the system can be used to compare data sets from different sources, data sets with different resolutions and data sets computed using different mathematical models of fluid flow. Suggested improvements to the system based on user feedback are also discussed.

We present extensions to the traditional cutting plane that become practical with the availability of virtual reality devices. These extensions take advantage of the intuitive ease of use associated with the cutting metaphor. Using their hands as the cutting tool, users interact directly with the data to generate arbitrarily oriented planar surfaces, linear surfaces, and curved surfaces. We find that this type of interaction is particularly suited for exploratory scientific visualization where features need to be quickly identified and precise positioning is not required. We also document our investigation on other intuitive metaphors for use with scientific visualization.

The paper presents interactive flow visualization methods that highlight directional information in the flow field. An added benefit of the proposed methods is that they reduce the amount of data being displayed and hence reduce clutter. The main idea behind these methods is the use of light sources to select and highlight regions in the flow field with similar directions. Varying the lighting conditions, by moving the light source and/or adding more lights, emphasizes different vector directions, set of directions, and vectors within a specified angle of a particular direction. The methods are straight forward, computationally inexpensive, and can be combined with other techniques that use glyph representation and other flow geometry such as streamlines for feature visualization. The authors apply these methods to an analytic data set to help explain how they work, and then to a simulation data set to highlight flow reversals.

Uncertainty or errors are introduced in fluid flow data as the data is acquired, transformed and rendered. Although researchers are aware of these uncertainties, little has been done to incorporate them in the existing visualization systems for fluid flow. In the absence of integrated presentation of data and its associated uncertainty, the analysis of the visualization is incomplete at best and may lead to inaccurate or incorrect conclusions. The article presents UFLOW-a system for visualizing uncertainty in fluid flow. Although there are several sources of uncertainties in fluid flow data, in this work, we focus on uncertainty arising from the use of different numerical algorithms for computing particle traces in a fluid flow. The techniques that we have employed to visualize uncertainty in fluid flow include uncertainty glyphs, flow envelopes, animations, priority sequences, twirling batons of trace viewpoints, and rakes. These techniques are effective in making the users aware of the effects of different integration methods and their sensitivity, especially near critical points in the flow field.

Environmental data have inherent uncertainty which is often ignored in visualization. Meteorological stations and doppler radars, including their time series averages, have a wealth of uncertainty information that traditional vector visualization methods such as meteorological wind barbs and arrow glyphs simply ignore. We have developed a new vector glyph to visualize uncertainty in winds and ocean currents. Our approach is to include uncertainty in direction and magnitude, as well as the mean direction and length, in vector glyph plots. Our glyph shows the variation in uncertainty, and provides fair comparisons of data from instruments, models, and time averages of varying certainty. We also define visualizations that incorporate uncertainty in an unambiguous manner as verity visualization. We use both quantitative and qualitative methods to compare our glyphs to traditional ones. Subjective comparison tests with experts are provided, as well as objective tests, where the information density of our new glyphs and traditional glyphs are compared. The design of the glyph and numerous examples using environmental data are given. We show enhanced visualizations, data together with their uncertainty information, that may improve understanding of environmental vector field data quality.

We present an environment in which users can interactively create different visualization methods. This modular and extensible environment encapsulates most of the existing visualization algorithms. Users can easily construct new visualization methods by combining simple, fine grain building blocks. These components operate on a local subset of the data and generally either look for target features or produce visual objects. Intermediate compositions may also be used to build more complex visualizations. This environment provides a foundation for building and exploring novel visualization methods.>

We propose a new framework for doing scientific visualization. The basis for this framework is a combination of particle systems and behavioral animation. Here, particles are not only affected by the field that they are in, but can also exhibit different programmed behaviors. An intuitive delivery system, based on virtual cans of spray paint, is also described to introduce the smart particles into the data set. Hence the name spray rendering. Using this metaphor, different types of spray paint are used to highlight different features in the data set. Spray rendering offers several advantages over existing methods: (1) it generalizes the current techniques of surface, volume and flow visualization under one coherent framework; (2) it works with regular and irregular grids as well as sparse and dense data sets; (3) it allows selective progressive refinement; (4) it is modular, extensible and provides scientists with the flexibility for exploring relationships in their data sets in natural and artistic ways.>

The authors present recent results obtained from simulating the action potential generation and propagation through a thin 3-D cylindrical heart muscle bundle. These results represent the first step towards obtaining a more accurate understanding of wave propagation properties through striated muscles. Using a 3-D computational grid of 32*32*128 cells, a single muscle bundle was modeled. The individual fibers and inter-fiber matter making up the bundle were treated homogeneously. Using a modified FitzHugh-Nagumo equation called the Epsilon-4 model, the authors obtained the relationship of propagation speed as a function of cross-sectional depth. The variations in speed that were observed can be explained in terms of the different wave curvatures arising from different types of boundary conditions and the geometry of the pathway. From these observations, the authors construct experiments where unidirectional blocks can occur in geometries with low excitability and no flux borders.>