On Wednesday, 10 May 2017 22:25:13 UTC+10, Paul Leopardi wrote: > > I have just completed the first draft of a paper, "Classifying bent > functions by their Cayley graphs". > <https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fsite%2Fpaulleopardi%2FLeopardi-Bent-functions-Cayley-graphs.pdf%3Fattredirects%3D0%26d%3D1&sa=D&sntz=1&usg=AFQjCNHCgXd-J3pgfrZkEYLA9E7JMgHABA> > > The computational results of the paper are fully reproducible via worksheets > in a SageMathCloud public folder > <https://cloud.sagemath.com/projects/80f4c9e7-8a37-4f59-82e7-aa179ec0b652/files/Boolean-Cayley-graphs/> > > and Sage code in a GitHub repository > <https://github.com/penguian/Boolean-Cayley-graphs>. >
Thanks all. I have now completed the fifth draft of "Classifying bent functions by their Cayley graphs" <https://arxiv.org/abs/1705.04507>, have uploaded it to arXiv as https://arxiv.org/abs/1705.04507 and have submitted it to INTEGERS: The Electronic Journal of Combinatorial Number Theory <http://math.colgate.edu/~integers/>. As well as the worksheets in a SageMathCloud public folder <https://cocalc.com/projects/80f4c9e7-8a37-4f59-82e7-aa179ec0b652/files/Boolean-Cayley-graphs/> and Sage code in a GitHub repository <https://github.com/penguian/Boolean-Cayley-graphs>, I have also uploaded a draft of the Python documentation to SourceForge <https://boolean-cayley-graphs.sourceforge.io/>. The code now includes Python functions that populate and query SQL databases of classifications, using either PostgreSQL <https://boolean-cayley-graphs.sourceforge.io/boolean_cayley_graphs.classification_database_psycopg2.html> or SQLite <https://boolean-cayley-graphs.sourceforge.io/boolean_cayley_graphs.classification_database_sqlite3.html>. As stated in the paper, I have used the code with MPI4Py on NCI Raijin <http://nci.org.au/systems-services/peak-system/raijin/> to classify the 256 bent functions from the S-boxes of CAST-128 <https://tools.ietf.org/html/rfc2144>, and the 5442 partial spread (+) bent functions in 8 dimensions <http://langevin.univ-tln.fr/project/spread/psp.html>. These results are too large to upload to CoCalc (2.1 TB in the case of the partial spread functions), but I am currently planning to use the Nectar cloud <https://nectar.org.au/> to host a prototype database server, at least for the CAST-128 results. I am now looking for: 1. Someone to review the code and suggest which parts should be pushed upstream into Sage. 2. Someone to try to replicate my results, along the lines of ReScience <http://rescience.github.io/>. 3. Someone to help and advise on how to build a database hosting platform. I was thinking of starting with the CoCalc Docker image <https://github.com/sagemathinc/cocalc-docker> and using PostgreSQL, then adding front end functionality based on LMFDB <http://www.lmfdb.org/>. -- You received this message because you are subscribed to the Google Groups "sage-devel" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/sage-devel. For more options, visit https://groups.google.com/d/optout.
