I was trying to point to the absurdity of the debate, even as someone who (moderately) opposes gun control laws. And I think we actually agree with each other on the "scientific" basis of my comments. I view the logic behind the debate in a similar way that a drunk driver might say, "well, if I was even more intoxicated, then I wouldn't have caused that accident". It doesn't really make sense to think that way. But the narrative of putting more guns into schools as a benefit has been pushed so hard that it actually makes me question the potential positive outcome (as opposed to outright rejecting it based on the preponderance of evidence to the contrary).
I've always found this debate interesting. I own a few guns and don't want to see my rights restricted. But I also don't feel comfortable with the thought of my kid's teacher carrying a gun. And while I don't dismiss the possibility of armed teachers being a deterrent for school shootings, I do question the fact that so many other countries that have more restrictive gun control laws also have far fewer homicide rates (especially in mass shooting situations).
As a scientist, it feels like I'm looking at a dataset of the rest of the world that shows a strong correlation between strict gun control and lower homicide rates. But then I say to myself, "maybe we (America) can have less gun control, which will cause us to exhibit even lower homicide rates". But I know that thought doesn't really make sense.
Yep. Every sensor will receive the output from the other emitters. This problem is actually pretty similar to the problem of sharing the wireless signal spectrum - how do all the cell phones in one area work at the same time? There's a few standard approaches to solving these problems:
- Frequency Division: each detector uses a different frequency/wavelength. This approach is very simple to implement, but the usable range of wavelengths for LIDAR is fairly narrow. So we'd run out of choices pretty quickly.
- Time Division: each detector is allocated a different operating time slot, so that only one is active at a time. For cell phones, this is relatively easy to implement because they all connect to a central system that can coordinate the timing. But for cars, this would be more difficult since there's no central system that links the detectors on different cars together (although they could still communicate with each other through other means).
- Code Division: each detector's output is pulsed in a unique pattern so that the reflected signals will return that same pattern, then the processor can reject any patterns that it didn't send out. This approach is much more complex, but doesn't suffer from many of the drawbacks of the other solutions.
Code division is by far the most widely used approach here, but frequency and time division also play a small role as well since different manufacturers/detectors use different wavelengths and not all detectors are being operated at exactly the same time (the time needed to send and receive a single signal is extremely short).
The largest barrier to accelerated life testing right now is that we really don't know all the mechanisms that cause degradation (or understand their impact on stability). There are many lab instruments that already provide HALT metrics on how stable a drug sample is expected to be under different conditions. And the Arrhenius and Eyring equations are useful for empirical modeling. These tools are great for quickly identifying when formulations are unstable. But it's very difficult to say with certainty (at least in the pharma world) that something will be stable for X years unless you actually test it for X years.
For the vast majority of manufactured products, HALT makes sense because any uncertainty that remains after testing is not likely to harm users. But with some of these drugs, any uncertainty, even after HALT, could cause major problems for users. And it seems that the FDA is unwilling to accept that risk. But as far as I know, the FDA is actively seeking methods for performing HALT that produce accurate and repeatable results.
> It reveals that you are more likely to become an olympic medalist if you are from the Caribbean, Scandinavia, certain Eastern European countries, Australia and New Zealand, than if you are Chinese, American or Russian.
Sure. But is that the same as "producing great athletes"? Many of the Caribbean nations also have populations that are ~100x smaller than the US, Russia, or China. Russia would need to win 430 medals (nearly half of the total medal count) just to compete with the Bahamas winning 1 medal based on this metric.
Caribbean countries tend to do very well in track and field sports, but they don't send many athletes to compete in other sports. So it might be more informative to see the number of olympic qualifiers for each country (not just medal count) per capita. But I'm not sure that would be much better at determining which country produces the best athletes.
There's so many confounding factors in the data that it's difficult to draw any real conclusions. I still find these kinds of statistics interesting though.
Not only was that sales clerk bad at his job (discouraging you from pursuing something that you're already interested in), but he was also very wrong.
I'm an electrical engineer who develops hardware similar to the Arduino for a living. I'm still constantly amazed by the things my friends, mostly artists and web designers, make using Arduinos and Raspberry Pis. I remember buying one of the first Arduinos and thinking, "Why did I even go to school? They've made everything so much easier." My friends still ask me questions and help them troubleshoot things; but for the most part, there are tutorials and forums that often provide more information that I can. Sparkfun and Adafruit have a lot of great beginner level tutorials, but they also have some really complex and challenging projects too.
I have a friend who is a realtor and we talk about this subject occasionally. My understanding is that most realtors will recommend using Zillow to "get a feel" for the market. But Zillow's estimates are only as good as the data that goes into them. Usually, data for an area is publicly available and up-to-date, but sometimes it's very old or just plain wrong.
I think Zillow claims their estimates to be within 10% of actual sale price. To me, that figure seem to be pretty good from a data analysis stand point; but 10% of the value of a house is a lot of money for even a modest home.
Most realtors used closed databases, like MLS, to document sales and compare area housing prices. I believe MLS data is typically manually entered and verified (I'm not 100% sure about this though). I believe there is also a lot of extra metadata added to MLS listings that Zillow can't get from public records. Realtors also tend to look at property tax assessments for their areas in order to gauge market trends (just like Zillow does, except without a hidden algorithm).
How does this happen? Are you talking about merge conflicts? Or actual releases that get deployed that don't work?