Our Research Recognized at 2023 IEEE Future Networks World Forum (FNWF)
We are pleased to share some news from the 2023 IEEE Future Networks World Forum (FNWF) in Baltimore, MD, USA. Our paper, mmWave Beam Selection in Analog Beamforming Using Personalized Federated Learning was awarded the Best Paper Award. This recognition is both humbling and encouraging for us as researchers.
Optimizing Your LaTeX Workflow: A Guide to Choosing a Build System
Long LaTeX build times can be a significant challenge for researchers and
developers, hampering productivity and efficiency. This issue arises due to the
complexity of LaTeX documents and the diversity of build systems available. We
present a comprehensive exploration of LaTeX build systems, helping authors
choose the most suitable one. By identifying the best build system, authors can
streamline their workflow, reduce build times, and ultimately enhance their
research and development endeavors.
Top LaTeX commands and macros for academic writing (and more)
LaTeX, a typesetting system celebrated for its capacity to blend
visual appeal with practicality, remains an the essential instrument for
researchers and academics. However, while its capabilities are impressive, the learning curve is steep. As a researcher in AI, I find that I
am very often using a set of LaTeX commands, macros and definitions when writing
academic papers, and perhaps you will find them useful too.
Publishing IEEE pre-prints
If you have submitted or plan to submit your paper to an IEEE journal or conference, you might want to consider posting your pre-print in arXiv.org or TechRxiv.org, on your employer’s website or institutional repository and on your personal website. IEEE does not consider this to be a form of prior publication, see IEEE Post-Publication Policies. But what are the practical steps to do so? In this post we cover the mandatory steps you have to take in order to publish an IEEE article as a pre-print.
Adaptive Expert Models for Personalization in Federated Learning →
Federated Learning (FL) is a promising framework for distributed learning when
data is private and sensitive. However, the state-of-the-art solutions in this
framework are not optimal when data is heterogeneous and non-Independent and
Identically Distributed (non-IID). We propose a practical and robust approach
to personalization in FL that adjusts to heterogeneous and non-IID data by
balancing exploration and exploitation of several global models. To achieve our
aim of personalization, we use a Mixture of Experts (MoE) that learns to group
clients that are similar to each other, while using the global models more
efficiently. We show that our approach achieves an accuracy up to 29.78 % and
up to 4.38 % better compared to a local model in a pathological non-IID
setting, even though we tune our approach in the IID setting.
Adding Sparklines to LaTeX tables using Pandas
Tables in scientific papers often look less than professional, and sometimes this can even get in the way of understanding the message. In this blog post we will learn how to add sparklines to a LaTeX table, which not only makes your table stand out, but also allows for conveying information about for example trends in time-series.
Create publication ready tables with Pandas
Tables in scientific papers often look less than professional, and
sometimes this can even get in the way of understanding the message. In this
blog post we will use pandas to automate making
publication ready LaTeX tables that look great.
Bootstrapping your next LaTeX project
The process of setting up a new LaTeX project is made up of many manual steps, resulting in a patchwork that already from the start is not exercisable nor complete. In this post we will see how we can construct a solid starting point with a single command. This is part of a series to create the perfect open science git repository.
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.