A new inductee in the Madagascar Hall of Fame is Jim Jennings.
You can read Jim’s story here.
The first ever worldwide Madagascar conference will take place on June 21-27, 2021. The participation is free of charge.
The conference program will be announced later. Meanwhile, please indicate the level of your interest in participation by filling a form on the website.
Several enhancements have been added to Madagascar’s Python interface.
Behind the scene, temporary files are created, and Madgascar programs run in the usual way, but, for the user, they appears like native Python functions. This way, the full power of Madagascar becomes available to people who prefer to work on data analysis projects in a Python environment.
SConstructscripts are written in Python, they are easy to adapt for including Python functions in place of command-line instructions. See an example of using Keras with SCons or an example of using PyTorch with SCons.
In deep learning projects, the training data, the neural-network model, and the testing data can be treated as files and handled effectively through SCons workflows while mixing with Madagascar commands and workflows.
Madagascar users are invited to try the new functionality and contribute to its further development.
We use least-squares migration to emphasize edge diffractions. The inverted forward modeling operator is the chain of three operators: Kirchhoff modeling, azimuthal plane-wave destruction and path-summation integral filter. Azimuthal plane-wave destruction removes reflected energy without damaging edge diffraction signatures. Path-summation integral guides the inversion towards probable diffraction locations. We combine sparsity constraints and anisotropic smoothing in the form of shaping regularization to highlight edge diffractions. Anisotropic smoothing enforces continuity along edges. Sparsity constraints emphasize diffractions perpendicular to edges and have a denoising effect. Synthetic and field data examples illustrate the effectiveness of the proposed approach in denoising and highlighting edge diffractions, such as channel edges and faults.
American Innovation and Competitiveness Act was adopted unanimously by the U.S. Congress and signed into law by president Obama in January 2017.
The law contains a section called Research Reproducibility and Replication, which asked the Director of the National Science Foundation in agreement with the National Research Council to prepare a report on issues related to research reproducibility and “to make recommendations for improving rigor and transparency in scientific research”.
To fulfill this requirement, a consensus report of the National Academies of Sciences, Engineering, and Medicine was published in 2019. The report is summarized in the special issue of Harvard Data Science Review in December 2020.
Among the recommendations:
All researchers should include a clear, specific, and complete description of how the reported results were reached. Reports should include details appropriate for the type of research, including:
Funding agencies and organizations should consider investing in research and development of open-source, usable tools and infrastructure that support reproducibility for a broad range of studies across different domains in a seamless fashion. Concurrently, investments would be helpful in outreach to inform and train researchers on best practices and how to use these tools.
Journals should consider ways to ensure computational reproducibility for publications that make claims based on computations, to the extent ethically and legally possible.