TY - CONF
AB - We present an efficient algorithm to compute Euler characteristic curves of gray scale images of arbitrary dimension. In various applications the Euler characteristic curve is used as a descriptor of an image. Our algorithm is the first streaming algorithm for Euler characteristic curves. The usage of streaming removes the necessity to store the entire image in RAM. Experiments show that our implementation handles terabyte scale images on commodity hardware. Due to lock-free parallelism, it scales well with the number of processor cores. Additionally, we put the concept of the Euler characteristic curve in the wider context of computational topology. In particular, we explain the connection with persistence diagrams.
AU - Heiss, Teresa
AU - Wagner, Hubert
ED - Felsberg, Michael
ED - Heyden, Anders
ED - Krüger, Norbert
ID - 833
SN - 03029743
TI - Streaming algorithm for Euler characteristic curves of multidimensional images
VL - 10424
ER -
TY - CONF
AB - Recent research has examined how to study the topological features of a continuous self-map by means of the persistence of the eigenspaces, for given eigenvalues, of the endomorphism induced in homology over a field. This raised the question of how to select dynamically significant eigenvalues. The present paper aims to answer this question, giving an algorithm that computes the persistence of eigenspaces for every eigenvalue simultaneously, also expressing said eigenspaces as direct sums of “finite” and “singular” subspaces.
AU - Ethier, Marc
AU - Jablonski, Grzegorz
AU - Mrozek, Marian
ID - 836
SN - 978-331956930-7
T2 - Special Sessions in Applications of Computer Algebra
TI - Finding eigenvalues of self-maps with the Kronecker canonical form
VL - 198
ER -
TY - CHAP
AB - The advent of high-throughput technologies and the concurrent advances in information sciences have led to a data revolution in biology. This revolution is most significant in molecular biology, with an increase in the number and scale of the “omics” projects over the last decade. Genomics projects, for example, have produced impressive advances in our knowledge of the information concealed into genomes, from the many genes that encode for the proteins that are responsible for most if not all cellular functions, to the noncoding regions that are now known to provide regulatory functions. Proteomics initiatives help to decipher the role of post-translation modifications on the protein structures and provide maps of protein-protein interactions, while functional genomics is the field that attempts to make use of the data produced by these projects to understand protein functions. The biggest challenge today is to assimilate the wealth of information provided by these initiatives into a conceptual framework that will help us decipher life. For example, the current views of the relationship between protein structure and function remain fragmented. We know of their sequences, more and more about their structures, we have information on their biological activities, but we have difficulties connecting this dotted line into an informed whole. We lack the experimental and computational tools for directly studying protein structure, function, and dynamics at the molecular and supra-molecular levels. In this chapter, we review some of the current developments in building the computational tools that are needed, focusing on the role that geometry and topology play in these efforts. One of our goals is to raise the general awareness about the importance of geometric methods in elucidating the mysterious foundations of our very existence. Another goal is the broadening of what we consider a geometric algorithm. There is plenty of valuable no-man’s-land between combinatorial and numerical algorithms, and it seems opportune to explore this land with a computational-geometric frame of mind.
AU - Edelsbrunner, Herbert
AU - Koehl, Patrice
ED - Toth, Csaba
ED - O'Rourke, Joseph
ED - Goodman, Jacob
ID - 84
T2 - Handbook of Discrete and Computational Geometry, Third Edition
TI - Computational topology for structural molecular biology
ER -
TY - JOUR
AB - Mapping every simplex in the Delaunay mosaic of a discrete point set to the radius of the smallest empty circumsphere gives a generalized discrete Morse function. Choosing the points from a Poisson point process in ℝ n , we study the expected number of simplices in the Delaunay mosaic as well as the expected number of critical simplices and nonsingular intervals in the corresponding generalized discrete gradient. Observing connections with other probabilistic models, we obtain precise expressions for the expected numbers in low dimensions. In particular, we obtain the expected numbers of simplices in the Poisson–Delaunay mosaic in dimensions n ≤ 4.
AU - Edelsbrunner, Herbert
AU - Nikitenko, Anton
AU - Reitzner, Matthias
ID - 718
IS - 3
JF - Advances in Applied Probability
SN - 00018678
TI - Expected sizes of poisson Delaunay mosaics and their discrete Morse functions
VL - 49
ER -
TY - JOUR
AB - We generalize Brazas’ topology on the fundamental group to the whole universal path space X˜ i.e., to the set of homotopy classes of all based paths. We develop basic properties of the new notion and provide a complete comparison of the obtained topology with the established topologies, in particular with the Lasso topology and the CO topology, i.e., the topology that is induced by the compact-open topology. It turns out that the new topology is the finest topology contained in the CO topology, for which the action of the fundamental group on the universal path space is a continuous group action.
AU - Virk, Ziga
AU - Zastrow, Andreas
ID - 737
JF - Topology and its Applications
SN - 01668641
TI - A new topology on the universal path space
VL - 231
ER -