Collections of analog lab notebooks are an invaluable source of data about research conditions, steps, and outcomes, and in aggregate have the potential to provide new insights into the successes, failures and pedagogy of research laboratories. Unfortunately, these artifacts are increasingly at risk of being lost from the historical scientific record, given limited archiving and an absence of computational and AI readiness. This paper reports on research addressing this challenge by testing mechanisms for transforming digital scans of analog lab notebooks into AI-ready data resources. The research being pursued is framed by the field of computational archival science (CAS) and the aim to utilize analog, research lab notebook data for scientific study. The paper presents background context on archival lab notebooks and CAS, discusses MOF (metal organic frameworks) and COF (covalent organic frameworks) synthesis – the scientific domain of the lab notebooks under study, and details our research methods. We demonstrate a promising approach that automatically segments pages into discrete entry types, extracts the contents of those entries, refines the output and assesses the automated results. These efforts represent a first step towards developing a framework for both improving the usability of archival lab notebooks, and enabling their contents to be used in subsequent scientific inquiry.

Shape analysis tasks, including mesh classification, segmentation, and retrieval demonstrate symmetries in Euclidean space and should be invariant to geometric transformations such as rotation and translation. However, existing methods in mesh analysis often rely on extensive data augmentation and more complex analysis models to handle 3D rotations. Despite these efforts, rotation invariance is not guaranteed, which can significantly reduce accuracy when test samples undergo arbitrary rotations, because the analysis method struggles to generalize to the unknown orientations of the test samples. To address these challenges, our work presents a novel approach that employs graph neural networks (GNNs) to analyze mesh-structured data. Our proposed GNN layer, aggregation function, and local pooling layer are equivariant to the rotation, reflection and translation of 3D shapes, making them suitable building blocks for our proposed rotation-invariant network for the classification of mesh models. Therefore, our proposed approach does not need rotation augmentation, and we can maintain accuracy even when test samples undergo arbitrary rotations. Extensive experiments on various datasets demonstrate that our methods achieve state-of-the-art performance.

Computational archival science (CAS) provides new pathways for research. Biologists, for example, can perform scientific studies by applying AI/ML to digital biological specimen collections and explore questions that were not possible in the analog world. One such approach is the application of computational methods for specimen outlining to assist with specimen identification, morphometry, and other scientific questions. The challenge is to determine how to computationally generate and represent a specimen’s outline. The research presented in this paper addresses this challenge, through the deployment of elliptical Fourier descriptors (EFDs). The paper describes the image processing pipeline for extracting fish outlines, a key morphological feature, and representing the outlines using EFDs. In addition, our research presents the application of machine learning classification on the EFDs. The resulting dataset is well suited for a variety of machine learning-based downstream analyses, including classification by genus and species. Overall, the classification tests produced a 96.3% accuracy, demonstrating the distinguishing nature of the EFDs, and by proxy, the fish outlines as a whole. Broadly, these results indicate the effectiveness of archival specimen usage in machine learning applications, and demonstrate specimen outlining via Fourier descriptors as a computational archival science approach.

Researchers seeking to apply computational methods are increasingly turning to scientific digital archives containing images of specimens. Unfortunately, metadata errors can inhibit the discovery and use of scientific archival images. One such case is the NSF-sponsored Biology Guided Neural Network (BGNN) project, where an abundance of metadata errors has significantly delayed development of a proposed, new class of neural networks. This paper reports on research addressing this challenge. We present a prototype workflow for specimen scientific name metadata verification that is grounded in Computational Archival Science (CAS), report on a taxonomy of specimen name metadata error types with preliminary solutions. Our 3-phased workflow includes tag extraction, text processing, and interactive assessment. A baseline test with the prototype workflow identified at least 15 scientific name metadata errors out of 857 manually reviewed, potentially erroneous specimen images, corresponding to a ∼0.2% error rate for the full image dataset. The prototype workflow minimizes the amount of time domain experts need to spend reviewing archive metadata for correctness and AI-readiness before these archival images can be utilized in downstream analysis.

We present a flexible, multi-agent approach to predictive classification problems which uses simple, modular agents that interact and share information socially in an arena with a variable number of participants. Opinion aggregation is accomplished using a honey-bee-derived optimization algorithm that improves accuracy and reduces variance compared with existing weighted and unweighted voter mechanisms. Confidence metrics may be derived from the agent interactions. We apply our system to a data set of 483 de-identified breast cancer patients to predict node-positive or node-negative disease with over 78.5% accuracy in general. When eliminating low-confidence predictions, which leaves 79.5% of patients, classification accuracy improves to 84.5%.

Whole slide images are examined by pathologists and scored according to the Gleason grading system. It is a time-consuming task and may involve assessing variability between different pathologists. In this work, a deep learning system is presented that generates classification maps for whole slide images. This system produces patch-level results first and then predicts a classification map for each prostate cancer slide. The classification maps contain regional cancer severity for each biopsy and are compared with provided mask images. Both provided mask images and predicted mask images are then reviewed by an experienced pathologist to evaluate classification performance. Most state-of-the-art deep learning methods cannot explain how they output classification results. With this work’s classification maps, pathologists can see the regional classification results that explain the algorithm’s classification.

Thermoplastic and Fused Deposition Modeling (FDM) based 4D printing are rapidly expanding to allow for space- and material-saving 2D printed sheets morphing into 3D shapes when heated. However, to our knowledge, all the known examples are either origami-based models with obvious folding hinges, or beam-based models with holes on the morphing surfaces. Morphing continuous double-curvature surfaces remains a challenge, both in terms of a tailored toolpath-planning strategy and a computational model that simulates it. Additionally, neither approach takes surface texture as a design parameter in its computational pipeline. To extend the design space of FDM-based 4D printing, in Geodesy, we focus on the morphing of continuous double-curvature surfaces or surface textures. We suggest a unique tool path - printing thermoplastics along 2D closed geodesic paths to form a surface with one raised continuous double-curvature tiles when exposed to heat. The design space is further extended to more complex geometries composed of a network of rising tiles (i.e., surface textures). Both design components and the computational pipeline are explained in the paper, followed by several printed geometric examples.

We present a software library that aids in the design of mobile ad hoc networks (MANET). The OMAN design engine works by taking a specification of network requirements and objectives, and allocates resources which satisfy the input constraints and maximize the communication performance objective. The tool is used to explore networking design options and challenges, including: power control, adaptive modulation, flow control, scheduling, mobility, uncertainty in channel models, and cross-layer design. The unaddressed niche which OMAN seeks to fill is the general framework for optimization of any network resource, under arbitrary constraints, and with any selection of multiple objectives. While simulation is an important part of measuring the effectiveness of implemented optimization techniques, the novelty and focus of OMAN is on proposing novel network design algorithms, aggregating existing approaches, and providing a general framework for a network designer to test out new proposed resource allocation methods. In this paper, we present a high-level view of the OMAN architecture, review specific mathematical models used in the network representation, and show how OMAN is used to evaluate tradeoffs in MANET design. Specifically, we cover three case studies of optimization. The first case is robust power control under uncertain channel information for a single physical layer snapshot. The second case is scheduling with the availability of directional radiation patterns. The third case is optimizing topology through movement planning of relay nodes.

Motivated by the ability of living cells to form specific shapes and structures, we present a computational approach using distributed genetic programming to discover cell-cell interaction rules for automated shape composition. The key concept is to evolve local rules that direct virtual cells to produce a self-organizing behavior that leads to the formation of a macroscopic, user-de.ned shape. The interactions of the virtual cells, called Morphogenic Primitives (MPs), are based on chemotaxis-driven aggregation behaviors exhibited by actual living cells. Cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. MPs, though, do not attempt to completely mimic the behavior of real cells. The chemical fields are explicitly defined as mathematical functions and are not necessarily physically accurate. The functions are derived via a distributed genetic programming process. A fitness measure, based on the shape that emerges from the chemical-field-driven aggregation, determines which functions will be passed along to later generations. This paper describes the cell interactions of MPs and a distributed genetic programming method to discover the chemical fields needed to produce macroscopic shapes from simple aggregating primitives.

Complex graphs, ones containing thousands of nodes of high degree, are difficult to visualize. Displaying all of the nodes and edges of these graphs can create an incomprehensible cluttered output. This paper presents a simplification algorithm that may be applied to a complex graph in order to produce a controlled thinning of the graph. Using importance metrics, the simplification process removes nodes from the graph, leaving the central structure for visualization and evaluation. The simplification algorithm consists of two steps, calculation of the importance metrics and pruning. Several metrics based on various topological graph properties are described. The metrics are then used in a pruning process to simplify the graph. Nodes, along with their corresponding edges, are removed from the graph, while maintaining the graph's overall connectivity. This simplified graph provides a cleaner, more meaningful visual representation of the graph's structure; thus aiding the analysis of the graph's underlying data.

Motivated by the ability of living cells to form into specific shapes and structures, we present a new approach to shape modeling based on self-organizing primitives whose behaviors are derived via genetic programming. The key concept of our approach is that local interactions between the primitives direct them to come together into a macroscopic shape. The interactions of the primitives, called Morphogenic Primitives (MP), are based on the chemotaxis-driven aggregation behaviors exhibited by actual living cells. Here, cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. MPs, though, do not attempt to completely mimic the behavior of real cells. The chemical fields are explicitly defined as mathematical functions and are not necessarily physically accurate. The explicit mathematical form of the chemical field functions are derived via genetic programming (GP), an evolutionary computing process that evolves a population of functions. A fitness measure, based on the shape that emerges from the chemical-field-driven aggregation, determines which functions will be passed along to later generations. This paper describes the cell interactions of MPs and the GP-based method used to define the chemical field functions needed to produce user-specified shapes from simple aggregating primitives.

Creating surfaces with intricate small-scale features (microgeometry) and detail is an important task in geometric modeling and computer graphics. We present a model processing method capable of producing a wide variety of complex surface features based on displacement mapping and stochastic geometry. The latter is a branch of mathematics that analyzes and characterizes the statistical properties of spatial structures. The technique has been incorporated into an interactive modeling environment that supports the design of stochastic microgeometries. Additionally a tool has been developed that provides random exploration of the technique's entire parameter space by generating sample microgeometry over a broad range of values. We demonstrate the effectiveness of our technique by creating diverse, complex surface structures for a variety of geometric models, e.g. arrowheads, candy bars, busts, planets and coral.

We present a robust method for 3D reconstruction of closed surfaces from sparsely sampled parallel contours. A solution to this problem is especially important for medical segmentation, where manual contouring of 2D imaging scans is still extensively used. Our proposed method is based on a morphing process applied to neighboring contours that sweeps out a 3D surface. Our method is guaranteed to produce closed surfaces that exactly pass through the input contours, regardless of the topology of the reconstruction. Our general approach consecutively morphs between sets of input contours using an Eulerian formulation (i.e. fixed grid) augmented with Lagrangian particles (i.e. interface tracking). This is numerically accomplished by propagating the input contours as 2D level sets with carefully constructed continuous speed functions. Specifically this involves particle advection to estimate distances between the contours, monotonicity constrained spline interpolation to compute continuous speed functions without overshooting, and state-of-the-art numerical techniques for solving the level set equations. We demonstrate the robustness of our method on a variety of medical, topographic and synthetic data sets.

Abstract Level set models combine a low‐level volumetric representation, the mathematics of deformable implicit surfaces and powerful, robust numerical techniques to produce a novel approach to shape design. While these models offer many benefits, their large‐scale representation and numerical requirements create significant challenges when developing an interactive system. This paper describes the collection of techniques and algorithms (some new, some pre‐existing) needed to overcome these challenges and to create an interactive editing system for this new type of geometric model. We summarize the algorithms for producing level set input models and, more importantly, for localizing/minimizing computation during the editing process. These algorithms include distance calculations, scan conversion, closest point determination, fast marching methods, bounding box creation, fast and incremental mesh extraction, numerical integration and narrow band techniques. Together these algorithms provide the capabilities required for interactive editing of level set models.

We present a level set framework for implementing editing operators for surfaces. Level set models are deformable implicit surfaces where the deformation of the surface is controlled by a speed function in the level set partial differential equation. In this paper we define a collection of speed functions that produce a set of surface editing operators. The speed functions describe the velocity at each point on the evolving surface in the direction of the surface normal. All of the information needed to deform a surface is encapsulated in the speed function, providing a simple, unified computational framework. The user combines pre-defined building blocks to create the desired speed function. The surface editing operators are quickly computed and may be applied both regionally and globally. The level set framework offers several advantages. 1) By construction, self-intersection cannot occur, which guarantees the generation of physically-realizable, simple, closed surfaces. 2) Level set models easily change topological genus, and 3) are free of the edge connectivity and mesh quality problems associated with mesh models. We present five examples of surface editing operators: blending, smoothing, sharpening, openings/closings and embossing. We demonstrate their effectiveness on several scanned objects and scan-converted models.

We present a new algorithm for extracting adaptive multiresolution triangle meshes from volume datasets. The algorithm guarantees that the topological genus of the generated mesh is the same as the genus of the surface embedded in the volume dataset at all levels of detail. In addition to this "hard constraint" on the genus of the mesh, the user can choose to specify some number of soft geometric constraints, such as triangle aspect ratio, minimum or maximum total number of vertices, minimum and/or maximum triangle edge lengths, maximum magnitude of various error metrics per triangle or vertex, including maximum curvature (area) error, maximum distance to the surface, and others. The mesh extraction process is fully automatic and does not require manual adjusting of parameters to produce the desired results as long as the user does not specify incompatible constraints. The algorithm robustly handles special topological cases, such as trimmed surfaces (intersections of the surface with the volume boundary), and manifolds with multiple disconnected components (several closed surfaces embedded in the same volume dataset). The meshes may self-intersect at coarse resolutions. However, the self-intersections are corrected automatically as the resolution of the meshes increase. We show several examples of meshes extracted from complex volume datasets.

We present a powerful morphing technique based on level set methods, that can be combined with a variety of scan conversion/model processing techniques. Bringing these techniques together creates a general morphing approach that allows a user to morph a number of geometric model types in a single animation. We have developed techniques for converting several types of geometric models (polygonal meshes, CSG models and MRI scans) into distance volumes, the volumetric representation required by our level set morphing approach. The combination of these two capabilities allows a user to create a morphing sequence regardless of the model type of the source and target objects, freeing him/her to use whatever model type is appropriate for a particular animation.

We present a new approach to 3D shape metamorphosis. We express the interpolation of two shapes as a process where one shape deforms to maximize its similarity with another shape. The process incrementally optimizes an objective function while deforming an implicit surface model. We represent the deformable surface as a level set (iso-surface) of a densely sampled scalar function of three dimensions. Such level-set models have been shown to mimic conventional parametric deformable surface models by encoding surface movements as changes in the grayscale values of a volume data set. Thus, a well-founded mathematical structure leads to a set of procedures that describes how voxel values can be manipulated to create deformations that are represented as a sequence of volumes. The result is a 3D morphing method that offers several advantages over previous methods, including minimal need for user input, no model parameterization, flexible topology, and subvoxel accuracy.

We present a novel method to extract iso-surfaces from distance volumes. It generates high quality semi-regular multiresolution meshes of arbitrary topology. Our technique proceeds in two stages. First, a very coarse mesh with guaranteed topology is extracted. Subsequently an iterative multi-scale force-based solver refines the initial mesh into a semi-regular mesh with geometrically adaptive sampling rate and good aspect ratio triangles. The coarse mesh extraction is performed using a new approach we call surface wavefront propagation. A set of discrete iso-distance ribbons are rapidly built and connected while respecting the topology of the iso-surface implied by the data. Subsequent multi-scale refinement is driven by a simple force-based solver designed to combine good iso-surface fit and high quality sampling through reparameterization. In contrast to the Marching Cubes technique our output meshes adapt gracefully to the iso-surface geometry, have a natural multiresolution structure and good aspect ratio triangles, as demonstrated with a number of examples.

This panel will address the technical, economic and marketing hurdles that inhibit the widespread use of cloth/clothing modeling, design and animation technology.Many of the fundamental technologies needed to support cloth modeling have already been developed.Still, we do not see computer-animated characters wearing realistic clothing, and clothing designers are not creating new fashions with computer-based 3-D design systems.The CAD industry is capable of providing the systems for designing the Boeing 777, but almost none of the clothing we wear is designed with 3-D CAD tools.The panel will explore the reasons that the design of cloth and clothing, a material that has been a part of the human experience for thousands of years, has not yet been fully computerized.A variety of issues related to cloth/clothing modeling will be explored.Are there fundamental technological barriers preventing us from fully deploying computer technology when modeling, designing, or animating cloth/clothing?Have the technical problems been solved and our computers are just not fast enough?Do we simply need good user interfaces to solve this difficult modeling problem?Is realism always important when modeling cloth?Does computer-based manufacturing place special demands on cloth modeling?Are designers, artists and clothing makers resisting the computerization of their "black art"?Can the average user even afford to do cloth modeling?Is the technology too expensive?These are some of the questions that the panelists will address. Can We Get There From Here?: Current Challenges in Cloth Design, Modeling and AnimationOrganizer

Augmented reality deals with the problem of dynamically augmenting or enhancing (images or live video of) the real world with computer generated data (e.g., graphics of virtual objects). This poses two major problems: (a) determining the precise alignment of real and virtual coordinate frames for overlay, and (b) capturing the 3D environment including camera and object motions. The latter is important for interactive augmented reality applications where users can interact with both real and virtual objects. Here we address the problem of accurately tracking the 3D motion of a monocular camera in a known 3D environment and dynamically estimating the 3D camera location. We utilize fully automated landmark-based camera calibration to initialize the motion estimation and employ extended Kalman filter techniques to track landmarks and to estimate the camera location. The implementation of our approach has been proven to be efficient and robust and our system successfully tracks in real-tim...

This paper describes how the concept of imposing geometric constraints by minimizing cost functions may be used and extended to accomplish a variety of animated modelling tasks for computer graphics. In this approach a complex 3-D geometric problem is mapped into a scalar minimization formulation. The mapping provides a straightforward method for converting abstract geometric concepts into a construct that is easily computed. The minimization approach is demonstrated in three application areas: computer animation, visualization, and physically-based modelling. In the computer animation application, cost minimization may be used to generate motion paths and joint parameters for animated actors. The approach may also be used to generate deformable models that extract closed 3-D geometric models from volume data for visualization. In the final application, the approach provides the fundamental structure to a physically-based model of woven cloth. © 1997 John Wiley & Sons, Ltd.

Abstract We present several techniques for producing two visual and modeling effects in augmented reality. The first effect involves interactively calculating the occlusions between real and virtual objects. The second effect utilizes a collision detection algorithm to automatically move dynamic virtual objects until they come in contact with static real objects in augmented reality. All of the techniques utilize calibrated data derived from images of a real‐world environment.

Abstract This paper presents a system for constructing collaborative design applications based on distributed augmented reality. Augmented reality interfaces are a natural method for presenting computer‐based design by merging graphics with a view of the real world. Distribution enables users at remote sites to collaborate on design tasks. The users interactively control their local view, try out design options, and communicate design proposals. They share virtual graphical objects that substitute for real objects which are not yet physically created or are not yet placed into the real design environment. We describe the underlying augmented reality system and in particular how it has been extended in order to support multi‐user collaboration. The construction of distributed augmented reality applications is made easier by a separation of interface, interaction and distribution issues. An interior design application is used as an example to demonstrate the advantages of our approach.

Abstract Augmented reality involves the use of models and their associated renderings to supplement information in a real scene. In order for this information to be relevant or meaningful, the models must be positioned and displayed in such a way that they align with their corresponding real objects. For practical reasons this alignment cannot be known a priori, and cannot be hard‐wired into a system. Instead a simple, reliable alignment or calibration process is performed so that computer models can be accurately registered with their real‐life counterparts. We describe the design and implementation of such a process and we show how it can be used to create convincing interactions between real and virtual objects.

Augmented reality entails the use of models and their associated renderings to supplement information in a real scene. In order for this information to be relevant or meaningful, the models must be positioned and displayed in such a way that they blend into the real world in terms of alignments, perspectives, illuminations, etc. For practical reasons the information necessary to obtain this realistic blending cannot be known a priori, and cannot be hard wired into a system. Instead a number of calibration procedures are necessary so that the location and parameters of each of the system components are known. We identify the calibration steps necessary to build a computer model of the real world and then, using the monitor based augmented reality system developed at ECRC (GRASP) as an example, we describe each of the calibration processes. These processes determine the internal parameters of our imaging devices (scan converter, frame grabber, and video camera), as well as the geometric transformations that relate all of the physical objects of the system to a known world coordinate system.>

We demonstrate a physically-based technique for predicting the drape of a wide variety of woven fabrics. The approach exploits a theoretical model that explicitly represents the microstructure of woven cloth with interacting particles, rather than utilizing a continuum approximation. By testing a cloth sample in a Kawabata fabric testing device, we obtain data that is used to tune the model's energy functions, so that it reproduces the draping behavior of the original material. Photographs, comparing the drape of actual cloth with visualizations of simulation results, show that we are able to reliably model the unique large-scale draping characteristics of distinctly different fabric types.

The algorithm presented in this paper converts a CSG model into a representation for interactive display on an engineering workstation. Called Constructive Cubes, the algorithm extends the standard CSG-point classification algorithm and then employs a popular isosurface generation algorithm, Marching Cubes, to generate a list of polygons that approximates the surface of a CSG model. The polygons may then be interactively displayed, shaded and inspected on a workstation. The algorithm has many advantages over other CSG algorithms. It is straightforward to implement, requiring no complex surface intersection calculations. The algorithm provides an inherent flexibility that allows a user to balance the time/quality trade-off. It is designed to take advantage of current and future advances in both visualization and engineering workstation design.

We propose a new approach to the problem of generating a simple topologically-closed geometric model from a point-sampled volume data set. We call such a model a Geometrically Deformed Model or GDM. A GDM is created by placing a 'seed' model in the volume data set. The model is then deformed by a relaxation process that minimizes a set of constraints that provides a measure of how well the model fits the features in the data. Constraints are associated with each vertex in the model that control local deformation, interaction between the model and the data set, and the shape and topology of the model. Once generated, a GDM can be used for visualization, shape recognition, geometric measurements, or subjected to a series of geometric operations. This technique is of special importance because of the advent of nondestructive sensing equipment (CT, MRI) that generates point samples of true three-dimensional objects.

The authors propose a methodology that will extract a topologically closed geometric model from a two-dimensional image. This is accomplished by starting with a simple model that is already topologically closed and deforming the model, based on a set of constraints, so that the model grows (shrinks) to fit the feature within the image while maintaining its closed and locally simple nature. The initial model is a non-self-intersecting polygon that is either embedded in the feature or surrounds the feature. There is a cost function associated with every vertex that quantifies its deformation, the properties of simple polygons, and the relationship between noise and feature. The constraints embody local properties of simple polygons and the nature of the relationship between noise and the features in the image.>

A programming methodology that implements many object-oriented features within a conventional programming environment is described. The methodology was created during the development of a computer animation system, The Clockworks. The methodology supports such object-oriented features as objects with variables and methods, class hierarchies, variable and method inheritance, object instantiation, and message passing. The methodology does not employ any special keywords or language extensions, thus removing the need for a preprocessor or compiler. The methodology has been implemented in a C/Unix environment. This allows the environment and any system developed within it to be ported to a wide variety of computers which support Unix. The methodology provides many object-oriented features and associated benefits. It also provides all the benefits of a C/Unix environment, including portability, a rich variety of development tools, and efficiency.>

This paper describes a message-based object-oriented tool for exploring mathematically-based interactions which produce complex motions for computer animation. The tool has been implemented as an object in the object-oriented computer animation system The Clockworks. It supports the definition of complex interactions between geometric objects through the specification of messages to the interacting objects. Our approach is general, flexible and powerful. The tool itself is not hardcoded to a particular application. It simply sends the messages specified by the user. Messages are specified as strings which may be stored, modified and interpreted. Since the tool is part of The Clockworks it may utilize many of the powerful features of the system, including data structuring, mathematical, geometric modeling, and rendering objects. The tool has been used to explore a general spring and mass model, and the response of objects in a vector field.

The Clockworks is an object-oriented computer animation system developed at RPl's Center for Interactive Computer Graphics (CICG). The Clockworks has the ability to model and graphically simulate complex 3-D engineering processes, Its interactive capabilities also allow it be used as a design tool. Object-oriented concepts have been utilized in developing its high level architecture and its low level implementation. The Clockworks is defined as a collection of objects which communicate with the user and each other via messages. The actual implementation involved the creation of an object-oriented programming methodology using C and Unix. The complete system provides a rich research environment for exploring modelling, scripting and rendering. It also provides an interactive environment for design and a simulation environment for visual analysis of complex interacting structures.