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Explore topological data analysis in non-Euclidean spaces, focusing on techniques for applying TDA tools and their applications in detecting neural network backdoor attacks.
Innovative approach to biomedical image analysis using topological structures and uncertainty, enhancing segmentation accuracy and enabling interactive annotation for fine-scale delineation.
Explore bounds for higher topological complexities in unordered configuration spaces of trees and anchored configuration spaces of circles with two anchor points.
Explore topological applications in discrete geometry, focusing on mass partitions and Helly-type theorems. Learn about classical results and recent generalizations in these areas.
Explore multi-parameter persistent homology for time-series data analysis, featuring an exact formula for one-dimensional reduction and efficient computational methods using Fourier decomposition and Liouville torus.
Explore how topology and persistent homology uncover hidden structures in neural data, focusing on Betti curves and their applications in analyzing hippocampal, olfactory, and zebrafish brain activity.
Explore topological descriptors in shape comparison, focusing on augmented vs non-augmented types and their ability to faithfully represent simplicial complexes. Accessible discussion with interesting open questions and visuals.
Explore scalable computation of extremum graphs, a simplified topological descriptor for scalar functions, with applications in visualization and shape analysis. Learn efficient parallel algorithms for large datasets.
Generalizace persistentnà homologie pro barevné bodové mraky. Detekce prostorových interakcà mezi různými typy bodů s aplikacemi v biologii a medicÃnÄ›.
Innovative approach to shape reconstruction using weighted l_1-norm minimization, transforming triangulation into a convex optimization problem for smooth orientable manifolds in high-dimensional spaces.
Explores quantitative approaches to Latschev's theorem for topological manifold reconstruction, advancing finite sample analysis in data science and manifold learning applications.
Exploring optimization on matrix manifolds, introducing Riemannian Frank-Wolfe methods for constrained problems, and discussing applications in machine learning and mathematics.
Explore techniques for reconstructing metric spaces and embeddings from distance matrices, covering theoretical foundations, practical applications, and open problems in geometric data analysis.
Exploring topological concepts in digital sound synthesis, from oscillatory algorithms to wave equations and filter theory, with applications in computational audio generation.
Explore higher chordality in algebraic topology, covering community algebra, linear resolution, graphs, polytopes, and complex links. Gain insights into advanced mathematical concepts.
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