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Explore flexibility in weak solutions to the Monge-Ampere system via convex integration, covering isometric immersions and nonlinear elasticity with focus on Holder regularity in arbitrary dimensions.
Explore the local-to-global principle in tensor-triangulated categories, examining its role in object reconstruction and its connection to the Balmer spectrum's pointless topology.
Explore p-adic geometry through Riemann-Hilbert correspondence, covering perfectoid rings, prismatic cohomology, and sheaf theory in characteristic p and beyond.
Explore p-adic geometry through Riemann-Hilbert correspondence, covering local systems, Galois representations, Hodge theory, and cohomology in this advanced mathematics lecture.
Explore p-adic geometry through a Riemann-Hilbert correspondence, examining algebraic differential equations, perverse sheaves, and prismatic cohomology in nonarchimedean fields.
Explores the Riemann-Hilbert correspondence in complex and p-adic geometry, discussing its historical context, mathematical foundations, and recent developments in prismatic cohomology.
Explore Lambda-coalescents in populations with dormancy, examining genealogy and multiple ancestral line mergers through a three-season model of spring awakening, summer reproduction, and winter dormancy.
Explore dormancy mechanisms in cancer progression, their role in therapy resistance and metastasis, and mathematical models used to study these phenomena. Gain insights into open questions and potential research directions.
Explores convex geometry in blind deconvolution and matrix completion, analyzing dimensional factors in noise bounds and proposing alternative error scaling approaches for low-rank matrix recovery problems.
Explore optimal learning from data, including error recovery, discrete optimization, and handling noisy measurements. Insights on deep learning and stochastic settings provided.
Explores implicit regularization in deep learning through matrix and tensor factorizations, revealing tendencies towards low ranks. Discusses implications for generalization and potential improvements in neural network design.
Machine learned regularization techniques for solving inverse problems, focusing on image reconstruction from tomographic and blurred measurements. Explores data-driven approaches and discusses open mathematical challenges.
Explore the probability of Buffon's needle landing near Cantor sets, delving into unexpected connections with various mathematical fields and theories.
Explore planar incidence geometry and lens counting theory to understand projection theorems, with applications in curve arrangements and intersection patterns.
Explore fractional Poincaré-Sobolev inequalities using Harmonic Analysis, unifying and improving known results for doubling and non-doubling weights in mathematical analysis.
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