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Computational topology has recently known an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appears as a fundamental tool in this field.…

Statistics Theory · Mathematics 2013-05-28 Frédéric Chazal , Marc Glisse , Catherine Labruère , Bertrand Michel

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data. When a natural choice of the graph is not readily available from the data sets,…

Machine Learning · Computer Science 2019-05-22 Xiaowen Dong , Dorina Thanou , Michael Rabbat , Pascal Frossard

Specify a randomized algorithm that, given a very large graph or network, extracts a random subgraph. What can we learn about the input graph from a single subsample? We derive laws of large numbers for the sampler output, by relating…

Statistics Theory · Mathematics 2017-10-13 Peter Orbanz

It was found recently that processes of multidimensional tunneling are generally described at high energies by unstable semiclassical trajectories. We study two observational signatures related to the instability of trajectories. First, we…

Quantum Physics · Physics 2015-05-13 D. G. Levkov , A. G. Panin , S. M. Sibiryakov

We introduce models and algorithmic foundations for graph watermarking. Our frameworks include security definitions and proofs, as well as characterizations when graph watermarking is algorithmically feasible, in spite of the fact that the…

Multimedia · Computer Science 2016-06-01 David Eppstein , Michael T. Goodrich , Jenny Lam , Nil Mamano , Michael Mitzenmacher , Manuel Torres

A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science.…

Social and Information Networks · Computer Science 2020-08-11 Feng Xia , Jiaying Liu , Hansong Nie , Yonghao Fu , Liangtian Wan , Xiangjie Kong

Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural building blocks. These features tend to be shared by…

Social and Information Networks · Computer Science 2016-10-20 Ian Barnett , Nishant Malik , Marieke L. Kuijjer , Peter J. Mucha , Jukka-Pekka Onnela

The characterization of the Hamiltonian parameters defining a quantum walk is of paramount importance when performing a variety of tasks, from quantum communication to computation. When dealing with physical implementations of quantum…

Quantum Physics · Physics 2024-03-15 Ilaria Gianani , Claudia Benedetti

Signature verification is an authentication technique that considers handwritten signature as a biometric. From a biometric perspective this project made use of automatic means through an integration of intelligent algorithms to perform…

Signal Processing · Electrical Eng. & Systems 2018-07-30 Rozita Teymourzadeh , Martin kizito , Kok Wai Chan , Mok Vee Hoong

We characterize the signature of piecewise continuously differentiable paths transformed by a polynomial map in terms of the signature of the original path. For this aim, we define recursively an algebra homomorphism between two shuffle…

Rings and Algebras · Mathematics 2020-02-06 Laura Colmenarejo , Rosa Preiß

Signed networks provide a principled framework for representing systems in which interactions are not merely present or absent but qualitatively distinct: friendly or antagonistic, supportive or conflicting, excitatory or inhibitory. This…

Motivated by a recent series of diametrically opposed articles on the relative value of statistical methods for the analysis of path diagrams in the social sciences, we discuss from a primarily theoretical perspective selected fundamental…

Methodology · Statistics 2020-11-13 R. Dennis Cook , Liliana Forzani

Dynamic graph learning has gained significant attention as it offers a powerful means to model intricate interactions among entities across various real-world and scientific domains. Notably, graphs serve as effective representations for…

Machine Learning · Computer Science 2024-01-17 Sanaz Hasanzadeh Fard

Random graphs are more and more used for modeling real world networks such as evolutionary networks of proteins. For this purpose we look at two different models and analyze how properties like connectedness and degree distributions are…

Probability · Mathematics 2019-02-05 Klemens Taglieber , Uta Freiberg

Expected urban population doubling calls for a compelling theory of the city. Random walks and diffusions defined on spatial city graphs spot hidden areas of geographical isolation in the urban landscape going downhill. First--passage time…

Physics and Society · Physics 2010-03-02 Ph. Blanchard , D. Volchenkov

Paths of persistence diagrams provide a summary of the dynamic topological structure of a one-parameter family of metric spaces. These summaries can be used to study and characterize the dynamic shape of data such as swarming behavior in…

Algebraic Topology · Mathematics 2023-11-17 Chad Giusti , Darrick Lee

Deterministic equilibrium flows in transport networks can be investigated by means of Markov's processes defined on the dual graph representations of the network. Sustained movement patterns are generated by a subset of automorphisms of the…

Physics and Society · Physics 2007-10-30 D. Volchenkov , Ph. Blanchard

We introduce and prove basic results about several graph-theoretic notions relevant to the multiresolution analysis of flow graphs that represent the transfer of control in computer programs. We take a category-theoretical viewpoint to…

Combinatorics · Mathematics 2019-06-28 Steve Huntsman

In this work, we develop a pipeline that associates Persistence Diagrams to digital data via the most appropriate filtration for the type of data considered. Using a grid search approach, this pipeline determines optimal representation…

Computer Vision and Pattern Recognition · Computer Science 2023-09-28 Francesco Conti , Davide Moroni , Maria Antonietta Pascali

Distribution regression refers to the supervised learning problem where labels are only available for groups of inputs instead of individual inputs. In this paper, we develop a rigorous mathematical framework for distribution regression…

Machine Learning · Computer Science 2021-09-30 Maud Lemercier , Cristopher Salvi , Theodoros Damoulas , Edwin V. Bonilla , Terry Lyons