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.

  • Full replica symmetry breaking in p-spin-glass-like systems


    year of publication: 2017 arXiv

    • Disordered Systems and Neural Networks
    • Statistical Mechanics
  • A Study on the Product Set-Labeling of Graphs


    year of publication: 2017 arXiv

    • General Mathematics

    MSC 2010: 05C78

  • Non-Hermiticity Induced Flat Band


    year of publication: 2017 arXiv

    • Optics
    • Other Condensed Matter
  • Warehousing complex data from the Web


    year of publication: 2017 arXiv

    • Databases
  • Géométrie non-commutative, formule des traces et conducteur de Bloch


    year of publication: 2017 arXiv

    • Algebraic Geometry
    • K-Theory and Homology

    MSC 2010: 14A22 32S30

  • Identifying the QCD Phase Transitions via the Gravitational Wave Frequency


    year of publication: 2017 arXiv

    • High Energy Astrophysical Phenomena
  • Self-Taught Convolutional Neural Networks for Short Text Clustering


    year of publication: 2017 arXiv

    • Computation and Language
    • Information Retrieval
  • On amenability and groups of measurable maps


    year of publication: 2017 arXiv

    • Functional Analysis
    • Group Theory

    MSC 2010: 22A05 43A07

  • Enumeration of Fuss-Schröder paths


    year of publication: 2017 arXiv

    • Combinatorics
  • Limit density of 2D quantum walk: zeroes of the weight function


    year of publication: 2017 arXiv

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