Regardless of road quality, weather conditions are unpredictable.
When the rain or snow or haboob kicks in, I guess your car will pull over to the side of the road and you'll need to drive manually until conditions improve. We'll need to keep the steering wheel around for the foreseeable future.
It's OK. Leibnitz[1] (who independently invented calculus) would also have had to be doing pull-ups for calculus to not have been invented. Although Newton not having invented calculus might also have hampered his contributions to physics, which would have been a great loss.
Something I just learned about Leibnitz from that Wikipedia article - he was also a pioneer in computing:
"He became one of the most prolific inventors in the field of mechanical calculators. While working on adding automatic multiplication and division to Pascal's calculator, he was the first to describe a pinwheel calculator in 1685 and invented the Leibniz wheel, used in the arithmometer, the first mass-produced mechanical calculator. He also refined the binary number system, which is the foundation of all digital computers."
Nextdoor is essentially a nationwide service for people to share coyote sightings in their neighborhood. No matter where they are, people won't shut up about them.
It's asking for my email to check if my census data has been leaked? Email isn't even shown as a field in the leaked records. Is this site just harvesting emails?
> That is, signal processing had Nyquist's rates. And typically knows there is an underlying signal. Does ml have either?
What does this question mean? Every band-limited signal has a Nyquist rate. Most signals of interest are well-contained within some finite bandwidth (e.g., human voice). Sampling above this rate will get you very little.
If you're building an ML model to process a certain class of sampled signal and you know, for example, 99% of the signal energy falls within a certain frequency range, that should guide your choice of sample rate. If you're sampling at too high a rate, your input layers may have far more parameters than are needed or useful.
Whether or not a given ML input actually contains a signal of interest doesn't seem relevant to how you sample and preprocess the signal.
Most machine learning is not on a band limited signal. I've literally seen these tactics applied to demand forecasting. And I just can't square that they should.
Ah, I see what you mean. Yes, if you're not dealing with approximately bandlimited and sampled signals, then this wouldn't apply. The article is about embedded devices processing sensor data (microphones, motion/light sensors, accelerometers, etc.), and in those cases the signal of interest will often be bandlimited.
Well, natural signals that have an end arent band-limited either. It is a mathematical abstraction that approximates many real world scenarios well enough
But our perception of many things can effectively be band limited with no loss in generality to work with the data. I'm unconvinced this is the case in places ml is often used.
Note, I have to hedge and say I am not convinced they are inapplicable. Just not convinced they are applicable.
Also note, I hadn't gotten the article to load when I fired off my concern. I keep the concern, but ack that it is not applicable to this article.
As I said in another comment I find it hard to separate what is Signal processing, vs what's Information Theory vs what's is ML. I have heard the argument that "if its got trigonometry then its signal processing", or "if its 1 dimensional then its signal processing" I find these arguments pretty weak and unconvincing.
Officially I belong in the ML tribe but all of them are tackling pretty much the exact same problem, any breakthrough in one of them will translate to the others. The name of the topic has changed over the years, the fundamental problem has remained the same -- lets call it another name -- approximating/extracting an unknown function from samples.
I agree they are all related. Just push back on the applicability of some techniques in places we don't actually know there are signals. If that makes sense.
What is a "typical person"? As the article states, most of these people are 70+ years old.
It would be interesting to know the prevalence of these illnesses among the cohort of 70+ year old Italians, but that would require more searching/translating than I care to do now. But feel free have at it.
Note that the most of the people in this study were 70+ years old, and I have no idea what the typical prevalence of these diseases is among 70+ year old Italians.
I haven't been able to figure out from the public statements regarding blood pressure: does the high mortality correspond to treated BP, untreated BP, or both?
I'd be curious to know if their diabetes was well controlled prior to contracting the corona virus or if it was out of control and already a cause of risk. Did the corona virus _cause_ the problem, or just make it worse?
> Il numero medio di patologie osservate in questa popolazione è di 2.7 (mediana 2, Deviazione Standard 1.6)
It seems the above poses another question; is diabetes just confounding other underlying condition(s)?
Further, if most of the patients suffered from type 2 diabetes, it would likely correlate with older age in which case higher fatality rates are to be expected.
I was unable to find this info publicly available, but diabetes being one of the "2+ underlying conditions" seems probable.
As a type 1 diabetic I'm always a bit disappointed when diabetes is mentioned in an article without specifying the sub type. It makes me wonder if they only encountered type 2 diabetics, or other types as well.
Well some of those are of course found among primarily old people, but as I understand it there are plenty of young people who are in critical condition in Italy as well.
If you're working for a company where it's seen as "heroic" or "passionate" to come to work sick, you're in a toxic environment.
I've had a couple co-workers who perhaps see themselves as heroic for coming to work sick, but everyone else resents them and wishes they would just stay home.
