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]
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/
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?
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.
I've been doing some models for live betting on ice hockey, but am now thinking of working on models for other sports as well. Would be nice to discuss about this and other ideas on sports betting and fantasy sports! My contact information is on my profile.
I have previously used Explicit Semantic Analysis (ESA) algorithm for individual word similarity calculations. ESA uses as a basis the text of Wikipedia entries and its ontology as a source and worked quite OK.
Do you / does anyone know if there is an easy way to use word2vec to compare similarities of two different documents (think of TF-IDF & cosine similarity)? It is stated on the page that "The linearity of the vector operations seems to weakly hold also for the addition of several vectors, so it is possible to add several word or phrase vectors to form representation of short sentences [2]", but the referenced paper has not yet been published.
It would be super interesting if there was a simple way to compare the similarities of two documents using something like this.
You mention in the article that you developed the algorithm as a part of writing your MSc thesis. Is your thesis available on the Internet (as in pdf or in any other format)?
Interestingly enough, this is how some coffee shops operate in Tokyo. It costs $21 to rent a small private room with Internet access, computer and unlimited supply of coffee and soft drinks. According to article, these rooms are mainly used by unemployed people who can't afford to rent their own place.
I wonder when BitTorrent Inc decides to release also the back-end part of the system so that you don't have to publish your live stream only via live.bittorent.com. IIRC, Bram Cohen has stated in their forums that they'll release it when they get the back-end SW to good enough shape, and when they do my assumption is that the p2p live streaming scene will move rapidly to BitTorrent Live.
I have tried and played with SopCast, PPLive, StreamTorrent, AceStream and the likes, and based on my own experiences BitTorrent Live seems to be completely in its own category when it comes to performance, source lag / latency and how well the system seems to overall work (this is also what Bram concludes on the Q/A section at the very end of the video).
Too bad that they can't build BitTorrent Live client on top of WebRTC as the protocol relies on access to UDP sockets. It would be fantastic for adoption that users would not need to install a plug-in to be able to use BitTorrent Live.
I am at the moment evaluating various browser components for headless browsing and based on my own brief tests, the two best ones seem to be Chromium Embedded and QtWebKit (I would prefer to have C/C++ solution, so PhantomJS and alike are unfortunately out of question).
Does anyone have any experience / is Chromium Embedded any better than using the WebKit component that is part of Qt 5.0 (QtWebKit)? Both seem to use same JavaScript engine (V8) and it seems that using QtWebKit in headless browser scenarios in Linux environment seems to be a bit easier, but I'm kind of wondering why there seems to be more buzz around CE than QTWK. On obvious reason of course is that using QTWK in app "forces" you to use Qt in other parts of the app that can be a good / bad thing depending on the details.