mlla

If only English support is required eSpeak could be replaced with MisakiSwift, which is under Apache 2.0 https://github.com/mlalma/MisakiSwift

Dividend derivatives have been a thing for quite a while. When Covid lockdowns started in 2020, EU mandated that EU banks are not allowed to pay dividends and Société Générale took quite a hit: "SocGen said it lost about 200 million euros on products related to the cancellation of dividend payments in the trading business." [1]

Some more information about available dividend derivatives on EUREX: https://www.eurexchange.com/resource/blob/80940/d5a6e620224a...

[1] https://www.straitstimes.com/business/banking/socgen-posts-s...

I was wondering about Trabucco as well. His name is nowhere to be seen, but he was leading Alameda together with Caroline up until last summer.

If SBF is also going for plea bargain, will various government institutions ask from him to get some dirt on Tether (and on Binance)? Might be that they are more interested in campaign financing.

Good Q. You might like to check ScyllaDB written in C++, which is supposed to have considerably better performance than Cassandra (also low tail-latency) and a level of compatibility with it: https://www.scylladb.com/

One good resource for understanding on how Bitcoin works on a lower level is this article: http://www.samlewis.me/2017/06/a-peek-under-bitcoins-hood/

It goes through the details of creating a tiny Bitcoin client that can send transactions so that they will get included in the blockchain.

One option is to use Real Time Kinematics (RTK) GPS, which can provide accuracy close to few centimetres, see e.g. http://diydrones.com/profiles/blogs/navio-rtk-demonstration

Have you had any chance to compare your player strength values against other models to check how well they are aligned? You can find from Premier League's web site "Player Performance Index", e.g. for last season the top three players according to PL PPI were Harry Kane, Riyad Mahrez and Jamie Vardy.

Another obvious question is that have you checked your model against the odds on betting sites that provide "Draw-no-bet" bets since you are not yet taking into account draws?

This is done using Latent Dirichlet Allocation (LDA). The original algorithm was published by David Blei et al over ten years ago, link to the paper: http://machinelearning.wustl.edu/mlpapers/paper_files/BleiNJ...

There are many machine learning libraries that have good implementations of LDA (e.g. Gensim), so it should be "relatively" straightforward to create the topics and clustering based on the abstracts of the papers.