LaTeX writing as a constrained non-convex optimization problem
The rejection rate for papers in good conferences is very high. To be accepted, a paper must not only be of a high scientific quality, but also at first impression perceived to be - or risk being thrown in the recycling bin. In this post we construct a system that automatically optimizes one proxy metric for perceived quality, removing one small frustrating step of scientific paper authorship and hopefully avoiding the bin.
How to beat publisher PDF checks with LaTeX document unit testing
When submitting a scientific paper to a conference or a journal, there is
often a mandatory step of passing the automated PDF checks set up by that
publication. This step can often be nerve-racking and cause many hours of
LaTeX troubleshooting. In this post we will create a series of test cases to
catch these problems early in the writing process so that you can submit your
manuscript only once.
How to annoy your co-authors: a Gitlab CI pipeline for LaTeX
The process of writing a LaTeX document can be one full of manual steps, resulting in a patchwork document that is not exercisable nor complete. This makes it impossible to reproduce the document from code and data. In this post we will create a pipeline for compiling a LaTeX document that works both locally and using GitLab CI. This is part of a series to create the perfect open science git repository.
Top 10 LaTeX packages for academic writing
In academic writing with LaTeX there are a lot of things that can be frustrating to the author. For many of these things there exists many packages that can help alleviate this frustration, but it is hard to find them. In this post I list 10 of my favorite packages to help remove some of this frustation, and make your papers look nicer so that you have a higher probability of getting your paper accepted. Hopefully.
Reproducibility aspects of the Swedish COVID–19 estimate report
Researchers have called out for more transparency from The Public Health Agency of Sweden regarding the COVID-19 estimates for Sweden. Recently, a report has been released covering such estimates for the Stockholm region. Along the report, the code used for these estimates was uploaded to Github, which makes it possible for others to review
and critique the work. In this post we will take a look at the reproducibility aspects of this release. We find that it is possible to some extent reproduce the figures in the report, and we suggest many improvements to the repository.
Secure Federated Learning in 5G Mobile Networks →
Machine Learning (ML) is an important enabler for optimizing, securing and managing mobile networks. This leads to increased collection and processing of data from network functions, which in turn may increase threats to sensitive end-user information. Consequently, mechanisms to reduce threats to end-user privacy are needed to take full advantage of ML. We seamlessly integrate Federated Learning (FL) into the 3GPP 5G Network Data Analytics (NWDA) architecture, and add a Multi-Party Computation (MPC) protocol for protecting the confidentiality of local updates. We evaluate the protocol and find that it has much lower overhead than previous work, without affecting ML performance.