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.

  • Sasaki manifolds, Kaehler cone manifolds and biharmonic submanifolds


    year of publication: 2013 arXiv

    • Differential Geometry

    MSC 2010: 53C43 58E20

  • Raman spectroscopy as a versatile tool for studying the properties of graphene


    year of publication: 2013 arXiv

    • Materials Science
  • Perfect powers in Catalan and Narayana numbers


    year of publication: 2013 arXiv

    • Combinatorics
    • Number Theory
  • Learning Trajectory Preferences for Manipulators via Iterative Improvement


    year of publication: 2013 arXiv

    • Artificial Intelligence
    • Human-Computer Interaction
    • Robotics
  • Broken planar Skyrmions -- statics and dynamics


    year of publication: 2013 arXiv

  • On the tritronquée solutions of P$_I^2$


    year of publication: 2013 arXiv

    • Classical Analysis and ODEs
    • Mathematical Physics
  • Expansion for moments of regression quantiles with application to nonparametric testing


    year of publication: 2013 arXiv

    • Statistics Theory
  • OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage


    year of publication: 2013 arXiv

    • Information Theory
    • Machine Learning
    • Statistics Theory
  • Random Walks in Cones: the Case of Nonzero Drift


    year of publication: 2013 arXiv

    • Probability
  • Simultaneous global exact controllability of an arbitrary number of 1D bilinear Schrödinger equations


    year of publication: 2013 arXiv

    • Analysis of PDEs
    • Optimization and Control
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