Anaconda is a fantastic software distribution that has everything that you need to start your Data Science projects. It comes with its own package manager, conda, which includes its own command prompt. It is great to manage your environments and launch your tools. However, when you need more than one command prompt open (which happens quite often), it is very annoying to have them spread all over the place. Having them under a single window with a tab system would be much handier. Enter Windows Terminal.

In this post, I will share the steps that I followed to incorporate Anaconda’s Prompt into it.

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The federated learning landscape is evolving incredibly fast. This is in big part due to its fantastic community, which is pushing the boundaries of what decentralized analysis can do. Moreover, the intrinsic nature of this technology promotes collaborations across different parties. As one of these collaborations, I am thrilled to announce a partnership between vantage6 and DataSHIELD.

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I absolutely love the 76057 set (Spider-Man: Web Warriors Ultimate Bridge Battle). I think it is one of the best LEGO Marvel sets ever made. It has something for everyone: fantastic selection of minifigs, tons of play features, and a very solid, detailed bridge build. However, my least favorite part was the road of the bridge. The black plates made it look a bit too bland and artificial (even for a plastic toy).

On 2021, LEGO released a new type of road plates (set 60304), which are very easy to couple to other builds (in contrast to the previous versions). As soon as I saw them, I knew I had to try incorporating them into the original bridge. I must say I am very happy with the results!

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For this year’s May the 4th, I wanted to create something special. This is what I came up with: an All Terrain Armored Transport using the MINDSTORMS 51515 set, or AT-AT MS5 in short. There was a motorized AT-AT build in the Dark Side Developer Kit (9754). However, since it was released on 2000, I feel it was in urgent need of an update. What better than using the MINDSTORMS Robot Inventor set 51515. The robot was programmed using Python.

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Unfortunately, LEGO’s Avengers sets haven’t always been on par with their corresponding MCU counterparts. Particularly, I think it sucks that it was only until 2019 that we got a build of a Leviathan… from a movie that was released on 2012. Not only that, but I think that the build looks very small, almost like of a baby Leviathan.

Therefore, I decided to take matters on my own hand. Using pieces exclusively from two small Leviathans, I created a Super Leviathan.

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One of the projects that I have enjoyed the most as part of my job at IKNL has been the application of explainable machine learning to survival predictions. Well, today I woke up with the happy news that our latest study in this field has been accepted for publication in Scientific Reports!

The abstract is as follows:

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I am very happy to share that we have officially released VANTAGE6 version 2.0! This time, we named it after a Japanese vantage point: Harukas.

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In this project, I built an Armored Assault Tank using the MINDSTORMS 51515 set, or AAT MS5 in short. It is inspired by the Battle Droid on STAP of the Droid Developer Kit and the AAT of the Dark Side Developer Kit. Moreover, it is powered by the MINDSTORMS Robot Inventor set 51515 and programmed using Python.

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I had a complete blast (re)building the Battle Droid on STAP from the Droid Developer Kit (9748). Thus, the next natural step was to give a model of the Dark Side Developer Kit (9754) the same treatment. The original set came with three different booklets (1, 2, 3) with instructions to build a Droid Starfighter, a Destroyer Droid (aka Droideka), and an AT-AT. However, in contrast to the Droid Developer Kit, this set didn’t have a CD. Instead, the booklets had pictures of the alternative builds. Among these, you could find the Armored Assault Tank (or AAT for short).

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Answering many of the questions in the field of cancer informatics (and in health care in general), often requires researchers to incorporate data from different sources. Typically, this is done by centralizing the data. In other words, data are brought to the where the algorithms are. Unfortunately, this brings several organizational, operational, political, and ethical challenges, such as loss of data control, logistics of data transmission, data governance, and protection of patient privacy.

Federated learning has emerged as a technology with the potential to overcome these limitations. In this case, we flip things around and we bring the algorithms to where the data are. For this purpose, at IKNL we have developed our open-source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange – or VANTAGE6 for short.

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