37,529 research outputs found
Exploring Material Representations for Sparse Voxel DAGs
Ray tracing is a popular technique used in movies and video games to create compelling visuals. Ray traced computer images are increasingly becoming more realistic and almost indistinguishable from real-word images. Due to the complexity of scenes and the desire for high resolution images, ray tracing can become very expensive in terms of computation and memory. To address these concerns, researchers have examined data structures to efficiently store geometric and material information. Sparse voxel octrees (SVOs) and directed acyclic graphs (DAGs) have proven to be successful geometric data structures for reducing memory requirements. Moxel DAGs connect material properties to these geometric data structures, but experience limitations related to memory, build times, and render times. This thesis examines the efficacy of connecting an alternative material data structure to existing geometric representations.
The contributions of this thesis include the creation of a new material representation using hashing to accompany DAGs, a method to calculate surface normals using neighboring voxel data, and a demonstration and validation that DAGs can be used to super sample based on proximity. This thesis also validates the visual acuity from these methods via a user survey comparing different output images. In comparison to the Moxel DAG implementation, this work increases render time, but reduces build times and memory, and improves the visual quality of output images
Spanners for Geometric Intersection Graphs
Efficient algorithms are presented for constructing spanners in geometric
intersection graphs. For a unit ball graph in R^k, a (1+\epsilon)-spanner is
obtained using efficient partitioning of the space into hypercubes and solving
bichromatic closest pair problems. The spanner construction has almost
equivalent complexity to the construction of Euclidean minimum spanning trees.
The results are extended to arbitrary ball graphs with a sub-quadratic running
time.
For unit ball graphs, the spanners have a small separator decomposition which
can be used to obtain efficient algorithms for approximating proximity problems
like diameter and distance queries. The results on compressed quadtrees,
geometric graph separators, and diameter approximation might be of independent
interest.Comment: 16 pages, 5 figures, Late
Recurrence-based time series analysis by means of complex network methods
Complex networks are an important paradigm of modern complex systems sciences
which allows quantitatively assessing the structural properties of systems
composed of different interacting entities. During the last years, intensive
efforts have been spent on applying network-based concepts also for the
analysis of dynamically relevant higher-order statistical properties of time
series. Notably, many corresponding approaches are closely related with the
concept of recurrence in phase space. In this paper, we review recent
methodological advances in time series analysis based on complex networks, with
a special emphasis on methods founded on recurrence plots. The potentials and
limitations of the individual methods are discussed and illustrated for
paradigmatic examples of dynamical systems as well as for real-world time
series. Complex network measures are shown to provide information about
structural features of dynamical systems that are complementary to those
characterized by other methods of time series analysis and, hence,
substantially enrich the knowledge gathered from other existing (linear as well
as nonlinear) approaches.Comment: To be published in International Journal of Bifurcation and Chaos
(2011
The Geometric Block Model
To capture the inherent geometric features of many community detection
problems, we propose to use a new random graph model of communities that we
call a Geometric Block Model. The geometric block model generalizes the random
geometric graphs in the same way that the well-studied stochastic block model
generalizes the Erdos-Renyi random graphs. It is also a natural extension of
random community models inspired by the recent theoretical and practical
advancement in community detection. While being a topic of fundamental
theoretical interest, our main contribution is to show that many practical
community structures are better explained by the geometric block model. We also
show that a simple triangle-counting algorithm to detect communities in the
geometric block model is near-optimal. Indeed, even in the regime where the
average degree of the graph grows only logarithmically with the number of
vertices (sparse-graph), we show that this algorithm performs extremely well,
both theoretically and practically. In contrast, the triangle-counting
algorithm is far from being optimum for the stochastic block model. We simulate
our results on both real and synthetic datasets to show superior performance of
both the new model as well as our algorithm.Comment: A shorter version of this paper has appeared in 32nd AAAI Conference
on Artificial Intelligence. The AAAI proceedings version as well as the
previous version in arxiv contained some errors that have been corrected in
this versio
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