What is Problem Mining? This is a tool based on data of arXiv.org which periodically scans packets of full texts of papers hosted on arXiv and available through arXiv's bulk data access policy, with the aim to automatically detect papers containing discussions on open problems or conjectures. It then creates a list from the detected papers putting together papers' descriptive data and some short snippets extracted from the full texts displaying an information on open problems or conjectures presented in the paper. The papers appearing on this list are only references to the arXiv's versions and we do NOT store the actual papers (TeX sources or PDFs) on our servers. To read any of the papers in this list one needs to follow the arXiv link displayed on the papers' blocks.

You can search within the list using keywords, author names, and subject area. By a simple click of the Interesting button, you may anonymously indicate your interest in the problem. If you think the automatic extraction resulted in incorrect data, you may click on the False positive button instead. The Stats section shows the worldwide interest by our users on a specific problem.

  • Brane webs in the presence of an $O5^-$-plane and 4d class S theories of type D


    year of publication: 2016 arXiv

  • Sublinear signal production in a two-dimensional Keller-Segel-Stokes system


    year of publication: 2016 arXiv

    • Analysis of PDEs

    MSC 2010: 35A01 35K35 35Q35 35Q92 92C17

  • On the relativistic mass function and averaging in cosmology


    year of publication: 2016 arXiv

    • Cosmology and Nongalactic Astrophysics
  • Fundamental Groups and Euler Characteristics of Sphere-like Digital Images


    year of publication: 2016 arXiv

    • General Topology

    MSC 2010: 55P10 55Q05

  • A singular one-parameter family of solutions in cubic superstring field theory


    year of publication: 2016 arXiv

  • On semigroup rings with decreasing Hilbert function


    year of publication: 2016 arXiv

    • Commutative Algebra
  • Trainlets: Dictionary Learning in High Dimensions


    year of publication: 2016 arXiv

    • Computer Vision and Pattern Recognition
  • On a certain type of nonlinear hyperbolic equations derived from astrophysical problems


    year of publication: 2016 arXiv

    • Analysis of PDEs
  • Sharp moment and exponential tail estimates for U-statistics


    year of publication: 2016 arXiv

    • Statistics Theory
  • Topological full groups of etale groupoids


    year of publication: 2016 arXiv

    • Dynamical Systems
    • Group Theory
    • Operator Algebras
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