@inproceedings{2901,
abstract = { We introduce the M-modes problem for graphical models: predicting the M label configurations of highest probability that are at the same time local maxima of the probability landscape. M-modes have multiple possible applications: because they are intrinsically diverse, they provide a principled alternative to non-maximum suppression techniques for structured prediction, they can act as codebook vectors for quantizing the configuration space, or they can form component centers for mixture model approximation. We present two algorithms for solving the M-modes problem. The first algorithm solves the problem in polynomial time when the underlying graphical model is a simple chain. The second algorithm solves the problem for junction chains. In synthetic and real dataset, we demonstrate how M-modes can improve the performance of prediction. We also use the generated modes as a tool to understand the topography of the probability distribution of configurations, for example with relation to the training set size and amount of noise in the data. },
author = {Chen, Chao and Kolmogorov, Vladimir and Yan, Zhu and Metaxas, Dimitris and Lampert, Christoph},
location = {Scottsdale, AZ, United States},
pages = {161 -- 169},
publisher = {JMLR},
title = {{Computing the M most probable modes of a graphical model}},
volume = {31},
year = {2013},
}
@inproceedings{2906,
abstract = {Motivated by an application in cell biology, we describe an extension of the kinetic data structures framework from Delaunay triangulations to fixed-radius alpha complexes. Our algorithm is implemented
using CGAL, following the exact geometric computation paradigm. We report on several
techniques to accelerate the computation that turn our implementation applicable to the underlying biological
problem.},
author = {Kerber, Michael and Edelsbrunner, Herbert},
booktitle = {2013 Proceedings of the 15th Workshop on Algorithm Engineering and Experiments},
location = {New Orleans, LA, United States},
pages = {70 -- 77},
publisher = {Society of Industrial and Applied Mathematics},
title = {{3D kinetic alpha complexes and their implementation}},
doi = {10.1137/1.9781611972931.6},
year = {2013},
}
@article{2939,
abstract = {In this paper, we present the first output-sensitive algorithm to compute the persistence diagram of a filtered simplicial complex. For any Γ > 0, it returns only those homology classes with persistence at least Γ. Instead of the classical reduction via column operations, our algorithm performs rank computations on submatrices of the boundary matrix. For an arbitrary constant δ ∈ (0, 1), the running time is O (C (1 - δ) Γ R d (n) log n), where C (1 - δ) Γ is the number of homology classes with persistence at least (1 - δ) Γ, n is the total number of simplices in the complex, d its dimension, and R d (n) is the complexity of computing the rank of an n × n matrix with O (d n) nonzero entries. Depending on the choice of the rank algorithm, this yields a deterministic O (C (1 - δ) Γ n 2.376) algorithm, an O (C (1 - δ) Γ n 2.28) Las-Vegas algorithm, or an O (C (1 - δ) Γ n 2 + ε{lunate}) Monte-Carlo algorithm for an arbitrary ε{lunate} > 0. The space complexity of the Monte-Carlo version is bounded by O (d n) = O (n log n).},
author = {Chen, Chao and Kerber, Michael},
journal = {Computational Geometry: Theory and Applications},
number = {4},
pages = {435 -- 447},
publisher = {Elsevier},
title = {{An output sensitive algorithm for persistent homology}},
doi = {10.1016/j.comgeo.2012.02.010},
volume = {46},
year = {2013},
}
@inproceedings{2209,
abstract = {A straight skeleton is a well-known geometric structure, and several algorithms exist to construct the straight skeleton for a given polygon or planar straight-line graph. In this paper, we ask the reverse question: Given the straight skeleton (in form of a planar straight-line graph, with some rays to infinity), can we reconstruct a planar straight-line graph for which this was the straight skeleton? We show how to reduce this problem to the problem of finding a line that intersects a set of convex polygons. We can find these convex polygons and all such lines in $O(nlog n)$ time in the Real RAM computer model, where $n$ denotes the number of edges of the input graph. We also explain how our approach can be used for recognizing Voronoi diagrams of points, thereby completing a partial solution provided by Ash and Bolker in 1985.
},
author = {Biedl, Therese and Held, Martin and Huber, Stefan},
location = {St. Petersburg, Russia},
pages = {37 -- 46},
publisher = {IEEE},
title = {{Recognizing straight skeletons and Voronoi diagrams and reconstructing their input}},
doi = {10.1109/ISVD.2013.11},
year = {2013},
}
@inproceedings{2210,
abstract = {A straight skeleton is a well-known geometric structure, and several algorithms exist to construct the straight skeleton for a given polygon. In this paper, we ask the reverse question: Given the straight skeleton (in form of a tree with a drawing in the plane, but with the exact position of the leaves unspecified), can we reconstruct the polygon? We show that in most cases there exists at most one polygon; in the remaining case there is an infinite number of polygons determined by one angle that can range in an interval. We can find this (set of) polygon(s) in linear time in the Real RAM computer model.},
author = {Biedl, Therese and Held, Martin and Huber, Stefan},
booktitle = {29th European Workshop on Computational Geometry},
location = {Braunschweig, Germany},
pages = {95 -- 98},
publisher = {TU Braunschweig},
title = {{Reconstructing polygons from embedded straight skeletons}},
year = {2013},
}