It is commonly agreed that accounts receivable (AR) can be a source of financial difficulty for firms when they are not efficiently managed and are underperforming. Experience across multiple industries shows that effective management of AR and overall financial performance of firms are positively correlated. In this paper we address the problem of reducing outstanding receivables through improvements in the collections strategy. Specifically, we demonstrate how supervised learning can be used to build models for predicting the payment outcomes of newly-created invoices, thus enabling customized collection actions tailored for each invoice or customer. Our models can predict with high accuracy if an invoice will be paid on time or not and can provide estimates of the magnitude of the delay. We illustrate our techniques in the context of real-world transaction data from multiple firms. Finally, simulation results show that our approach can reduce collection time up to a factor of four compared to a baseline that is not model-driven.

We present a method for incrementally maintaining a global, bijective mapping between an arbitrary triangular input mesh and a dynamically changing domain mesh by use of a meta-mesh. The domain mesh is derived from the input mesh through an arbitrary sequence of local mesh connectivity operations. The mapping can be maintained independent of the choice of metrics for driving the simplification process to derive the domain mesh. In addition, due to the dynamic nature of the mapping, user post-processing can be applied to adjust the final domains.

We present a variational framework for rapid shape prototyping. The modeled shape is represented as a Catmull-Clark multiresolution subdivision surface which is interactively deformed by direct user input. Free-form design goals are formulated as constraints on the shape and the modeling problem is cast into a constrained optimization one. The focus of this paper is on handling multiresolution constraints of different kinds and on preserving surface details throughout the deformation process. Our approach eliminates the need for an explicit decomposition of the input model into frequency bands and the overhead associated with saving and restoring high-frequency detail after global shape fairing. Instead, we define a deformation vector field over the model and we optimize its energy. Surface details are considered as part of the rest shape and are preserved during free-form model editing. We explore approximating the solution of the optimization problem to various degrees to balance trade-offs between interactivity and accuracy of the results.

For traditional multimedia types, transcoding has proven successful in serving variations of the same object at different sizes and using different modalities. Transcoding, which is defined as a transformation that is used to convert multimedia content from one form to another, can be naturally extended to 3D data. By their very nature, 3D models are amenable to access through various representation modalities that typically imply trade-offs between complexity, interaction, and download times. In this paper we describe the design and development of an adaptive environment for rendering of 3D models over networks. This environment monitors the resources available and selects the appropriate transmission and representation modalities to match these resources.

Cutting and pasting to combine different elements into a common structure are widely used operations that have been successfully adapted to many media types. Surface design could also benefit from the availability of a general, robust, and efficient cut-and-paste tool, especially during the initial stages of design when a large space of alternatives needs to be explored. Techniques to support cut-and-paste operations for surfaces have been proposed in the past, but have been of limited usefulness due to constraints on the type of shapes supported and the lack of real-time interaction. In this paper, we describe a set of algorithms based on multiresolution subdivision surfaces that perform at interactive rates and enable intuitive cut-and-paste operations.

In this paper we describe a method for creating sharp features and trim regions on multiresolution subdivision surfaces along a set of user-defined curves. Operations such as engraving, embossing, and trimming are important in many surface modeling applications. Their implementation, however, is non-trivial due to computational, topological, and smoothness constraints that the underlying surface has to satisfy. The novelty of our work lies in the ability to create sharp features anywhere on a surface and in the fact that the resulting representation remains within the multiresolution subdivision framework. Preserving the original representation has the advantage that other operations applicable to multiresolution subdivision surfaces can subsequently be applied to the edited model. We also introduce an extended set of subdivision rules for Catmull-Clark surfaces that allows the creation of creases along diagonals of control mesh faces.

The creation of three-dimensional digital content by scanning real objects has become common practice in graphics applications for which visual quality is paramount, such as animation, e-commerce, and virtual museums. While a lot of attention has been devoted recently to the problem of accurately capturing the geometry of scanned objects, the acquisition of high-quality textures is equally important, but not as widely studied. In this paper, we focus on methods to construct accurate digital models of scanned objects by integrating high-quality texture and normal maps with geometric data. These methods are designed for use with inexpensive, electronic camera-based systems in which low-resolution range images and high-resolution intensity images are acquired. The resulting models are well-suited for interactive rendering on the latest-generation graphics hardware with support for bump mapping. Our contributions include new techniques for processing range, reflectance, and surface normal data, for image-based registration of scans, and for reconstructing high-quality textures for the output digital object.

Describes an environment for visualization and processing of low-dose, low-contrast electron micrographs of biological specimens. We focus on image selection, the first step in the process of reconstruction of the 3D structure of a specimen from its projections. Noise from a variety of sources makes automatic detection of particle positions a difficult task. New image acquisition devices and modern electron microscopy methods require the processing and rendering of very large images (50-100 million pixels). We describe techniques for processing large images, algorithms for detecting particle positions on compressed images using the crosspoint method, and methods for position refinement. EMMA, an interactive visualization environment for experimenting with particle identification methods is presented.