Ari Rappoport

What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities

Current social media research mainly focuses on temporal trends of the information flow and on the topology of the social graph that facilitates the propagation of information. In this paper we study the effect of the content of the idea on the information propagation. We present an efficient hybrid approach based on a linear regression for predicting the spread of an idea in a given time frame. We show that a combination of content features with temporal and topological features minimizes prediction error. Our algorithm is evaluated on Twitter hashtags extracted from a dataset of more than 400 million tweets. We analyze the contribution and the limitations of the various feature types to the spread of information, demonstrating that content aspects can be used as strong predictors thus should not be disregarded. We also study the dependencies between global features such as graph topology and content features.

Shape blending using the star-skeleton representation

Shape blending, the metamorphosis of one shape into another, is a central problem in two-dimensional computer animation. In spite of impressive uses of morphing in film and video productions, the problem is far from solved. In particular, shape blending still requires considerable manual effort. By decomposing two polygons into equivalent star-shaped pieces and a connecting skeleton, the paper presents a blending method which can represent polygon interiors, not just boundaries, and generate high-quality results with minimal user intervention. >

Simple constrained deformations for geometric modeling and interactive design

Deformations are a powerful tool for shape modeling and design. We present a new model for producing controlled spatial deformations, which we term Simple Constrained Deformations (Scodef). The user defines a set of constraint points, giving a desired displacement and radius of influence for each. Each constraint point determines a local B-spline basis function centered at the constraint point, falling to zero for points beyond the radius. The deformed image of any point in space is a blend of these basis functions, using a projection matrix computed to satisfy the constraints. The deformation operates on the whole space regardless of the representation of the objects embedded inside the space. The constraints directly influence the final shape of the deformed objects, and this shape can be fine-tuned by adjusting the radius of influence of each constraint point. The computations required by the technique can be done very efficiently, and real-time interactive deformation editing on current workstations is possible.

Image-Based Rendering for Non-Diffuse Synthetic Scenes

Most current image-based rendering methods operate under the assumption that all of the visible surfaces in the scene are opaque ideal diffuse (Lambertian) reflectors. This paper is concerned with image-based rendering of non-diffuse synthetic scenes. We introduce a new family of image-based scene representations and describe corresponding image-based rendering algorithms that are capable of handling general synthetic scenes containing not only diffuse reflectors, but also specular and glossy objects. Our image-based representation is based on layered depth images. It represents simultaneously and separately both view-independent scene information and view-dependent appearance information. The view-dependent information may be either extracted directly from our data-structures, or evaluated procedurally using an image-based analogue of ray tracing. We describe image-based rendering algorithms that recombine the two components together in a manner that produces a good approximation to the correct image from any viewing position. In addition to extending image-based rendering to non-diffuse synthetic scenes, our paper has an important methodological contribution: it places image-based rendering, light field rendering, and volume graphics in a common framework of discrete raster-based scene representations.

Hexahedral Mesh Generation using the Embedded Voronoi Graph

Abstract. This work presents a new approach for automatic hexahedral meshing, based on the embedded Voronoi graph. The embedded Voronoi graph contains the full symbolic information of the Voronoi diagram and the medial axis of the object, and a geometric approximation to the real geometry. The embedded Voronoi graph is used for decomposing the object, with the guiding principle that resulting sub-volumes are sweepable. Sub-volumes are meshed independently, and the resulting meshes are easily combined and smoothed to yield the final mesh. The approach presented here is general and automatic. It handles any volume, even if its medial axis is degenerate. The embedded Voronoi graph provides complete information regarding proximity and adjacency relationships between the entities of the volume. Hence, decomposition faces are determined unambiguously, without any further geometric computations. The sub-volumes computed by the algorithm are guaranteed to be well-defined and disjoint. The size of the decomposition is relatively small, since every sub-volume contains a different Voronoi face. Mesh quality seems high since the decomposition avoids generation of sharp angles, and sweep and other basic methods are used to mesh the sub-volumes.

Efficient Unsupervised Discovery of Word Categories Using Symmetric Patterns and High Frequency Words

We present a novel approach for discovering word categories, sets of words sharing a significant aspect of their meaning. We utilize meta-patterns of high-frequency words and content words in order to discover pattern candidates. Symmetric patterns are then identified using graph-based measures, and word categories are created based on graph clique sets. Our method is the first pattern-based method that requires no corpus annotation or manually provided seed patterns or words. We evaluate our algorithm on very large corpora in two languages, using both human judgments and WordNet-based evaluation. Our fully unsupervised results are superior to previous work that used a POS tagged corpus, and computation time for huge corpora are orders of magnitude faster than previously reported.

Interactive reflections on curved objects

Global view-dependent illumination phenomena, in particular reflections, greatly enhance the realism of computer-generated imagery. Current interactive rendering methods do not provide satisfactory support for reflections on curved objects. In this paper we present a novel method for interactive computation of reflections on curved objects. We transform potentially reflected scene objects according to reflectors, to generate virtual objects. These are rendered by the graphics system as ordinary objects, creating a reflection image that is blended with the primary image. Virtual objects are created by tessellating scene objects and computing a virtual vertex for each resulting scene vertex. Virtual vertices are computed using a novel space subdivision, the reflection subdivision. For general polygonal mesh reflectors, we present an associated approximate acceleration scheme, the explosion map. For specific types of objects (e.g., linear extrusions of planar curves) the reflection subdivision can be reduced to a 2-D one that is utilized more accurately and efficiently. CR Categories: I.3.3 [Computer Graphics]: Picture/Image Generation; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism.

An architecture for universal CAD data exchange

Parametric feature-based CAD data exchange is one of the most important open problems in solid modeling. The problem is significant and challenging both scientifically and commercially. In this paper we present a very general outline of the Universal Product Representation (UPR) architecture, which provides universal support for all data levels employed by todays CAD systems. The architecture has been implemented with successful results.

Interactive Boolean operations for conceptual design of 3-D solids

Interactive modeling of 3-D solids is an important and difficult problem in computer graphics. The Constructive Solid Geometry (CSG) modeling scheme is highly attractive for interactive design, due to its support for hierarchical modeling and Boolean operations. Unfortunately, current algorithms for interactive display of CSG models require expensive special-purpose hardware that is not easily available. In this paper we present a method for interactive display of CSG models using standard, widely available graphics hardware. The method enables the user to interactively modify the affine transformations associated with CSG sub-objects. The application we focus upon is that of conceptual design, a stage in the design process in which rapid, interactive visualization of the model and high-level design operations are of crucial importance, while the objects are relatively simple. The method converts the CSG graph to a novel Convex Differences Aggregate(CDA) representation. The CDA utilizes graph re-writing techniques, efficient geometric algorithms on convex objects and a built-in hierarchical acceleration scheme. The CDA rendering algorithm is very simple, takes advantage of standard graphics hardware, and makes efficient use of system resources by splitting the work between the graphics system and the CPU. CR

Using Classifier Features for Studying the Effect of Native Language on the Choice of Written Second Language Words

We apply machine learning techniques to study language transfer, a major topic in the theory of Second Language Acquisition (SLA). Using an SVM for the problem of native language classification, we show that a careful analysis of the effects of various features can lead to scientific insights. In particular, we demonstrate that character bigrams alone allow classification levels of about 66% for a 5-class task, even when content and function word differences are accounted for. This may show that native language has a strong effect on the word choice of people writing in a second language.