That’s right, it’s a pretty thin layer. Parses the sheet and schedules the midi to be sent to a particular (virtual) device. Having it all in one place sounds a lot more convenient.
A particularly intriguing (to me) version of this kind of
user-centered copy is not uncommon on sites in French and uses first-person verbs: “I accept,” “I start”. As opposed to the infinitive “Comment,” “Sign in,” etc.
Find places online, then call to reserve. Just last week I called a place I found on Booking and found they had a room at half the price shown online. This is in France though where there seem to be rules about how rates are advertised.
I adopted a few tricks from either [1] or a similar video from James Hoffman’s Youtube channel some time ago (including the foaming milk with a French press one) and it vastly improved my lockdowns. The one useful thing they don’t say is (imo) it’s easier to get it right with a smaller size pot.
I don’t remember if the paper lists it among its limitations but it’s worth noting there is a pretty sharp continuity in Billboard’s methodology around 1991 (they didn’t really include any rap before that) that will warp this kind of analysis if you don’t control for it. This is sort of known among musicologists of pop music and often glossed over by this kind of research
Doing some form of PQ at train time is possible but typically the goal is to make the model’s embedding layers more robust to quantisation [1][2]. I did some work on this in the recommender systems space [3].
The author is still underselling the significance of the progress made during the first years IMO. The simple idea that is still behind most practical recommender systems (using gradient descent to do SVD to complete the rating matrix) was first described in 2006 by Simon Funk [1]. Koren, who ended up taking home a big part of the prize, recently wrote another paper about how that basic idea still outperforms most “AI” (deep neural) recommenders today [2].
The consensus here on this kind of issues these days seems to be that it’s not Google’s fault. However if their rights model would make a correct and consistent distinction between works and performance, they would know that there is no right holder to the work (melody) they’ve identified. It’s not impossible that there are copyright trolls spamming their database with performances of public domain works and claiming both type of rights, but than they should have a system to flag those as inconsistent with known public domain material. They have the money to clean up their data, it’s just not as fun as iterating on their audio to performance and audio to work ML systems, which seem to work fine
The elephant was built by French theater company Royale de Luxe in 2005 for a performance in France, before it traveled to London and elsewhere in 2006 [1]. It looks like two people who worked for them later started The Machines of the Isle of Nantes, and built the replica [2].
"The effect size of surgeons’ birthday observed in our analysis (1.3 percentage point increase or a 23% increase in mortality), though substantial, is comparable to the impact of other events, including holidays (e.g., Christmas and New Year) and weekends. (...) But the authors say the “natural experiment” in the present study is more revealing than, for example, holiday-related mortality rates. That is because “those events not only affect physicians’ performance but also influence patients’ decision to seek care (i.e., patients seeking care on these special days might be sicker than those seeking care on other days), as well as hospital staffing.” Unless, of course, the patients know their surgeon’s birthday, which is unlikely (though that may change if this study becomes widely known)."
That doesn't take exclude the possibility that surgeons may be assigned different patients on their birthdays. Some studies on the 'weekend effect' [1] seem to also control for illness severity, not clear if that was done here.
[1] https://en.wikipedia.org/wiki/Weekend_effect