> You won’t see these threads about Zurich, just saying.
Nope. Taking a train from Zürich HB to Bellinzona for the holiday. The train is full. My suitcase is in the next car.
It will be there, untouched, when I get off in an hour or so.
1,776 karma · joined March 2, 2018
Nope. Taking a train from Zürich HB to Bellinzona for the holiday. The train is full. My suitcase is in the next car.
It will be there, untouched, when I get off in an hour or so.
Post WW2 suburban communities had small roads (road width is a major factor in, i.e., how fast people feel safe driving), sidewalks, and - in general - were designed for a higher population density because this is what people wanted and were used to.
Over time, development optimised for housing and garage space with lower population densities. No one wants to go outside and walk or play if there’s no where to go.
The average suburban family in the 1980s had a car. They also had well maintained sidewalks, civic organisations, and - in many cases - stores within walking or biking distance.
A (bad) way to think of it would be something along the lines of a New England townhall system on steroids, with several layers of federal/confederated associations above the local township (geminde).
Again, not a great comparison, but it's something that at least a portion of the US-centric users on here would know a little about.
A counter argument (trigger warning): https://www.reddit.com/r/nursing/comments/zls30p/cosleeping/
We co-slept with our second child, but only after taking a lot of precautions.
Not true. Show me where in "On Guerrilla Warfare" Zedong - an expert on the American and French Revolutions - claims that guerrilla warfare requires external support.
Guerrilla warfare requires the support of the population (pretty much all US IW joint doctrine highlights this).
Economic power facilitates guerrilla warfare, along with the transition to traditional warfare, but it isn't a requirement.
> Washington had to keep British costs high while not losing, he couldn't just sit.
The only thing Washington had to do was survive.
50k+ lines of R, 10k+ lines of Julia, 5k+ in Python, C, and who knows what else. Most of it for what is, essentially, data engineering work.
Where do researchers with social science degrees fall on this scale? Less money, less clout. The projects are certainly interesting though (which is why I do what I do).
Anyway, I'm going to go back to my 5K+ lines of code for an upcoming conference submission - almost all of which involve data cleaning and aggregation - and think about how I could be making a 2x more than I am now.
Thanks Hacker News.
I will say, however, that we need to be very clear about making a distinction between bad methods and intentional malfeasance. Academic research generally involved a small team of researchers, at best. The code and amount of overall effort involved for what the earlier poster described is extremely intense. For better or worse, code QC simply doesn't exist in most departments for largely financial reasons.
You can generally expect that - most - efforts fall short somewhere, simply because the odds of not making a mistake or two in a 15K+ code base with one or two non-professional programmers are almost nil.
The hope is that another team in the future will tackle a similar task and, in the process, obtain results that are or are not consistent with what were previously published. Thankfully, if the question is interesting or impactful enough, and climate research certainly is, the aggregate body of evidence will eventually force out unintentionally bad research.
Of course, open data and code requirements facilitate this.
The mental impact of paying three-four figures a year for a subscription is different from a 3.99 app store payment.
This will benefit some folks. I imagine that most people will simply pass, the same way they’ve been passing on all of the other non-Tesla car subscription/remote upgrade offers.
Harder for BEVs where everything is electronically controlled. It’s a different story if there’s a physical motor and you can edit or bypass the ECU.
One of the strategies I hear about online is working with the dealership to take out financing with the implicit understanding that you’ll wait to pay off the loan until after they’ve received their payout.
Now, cash vs financing is a different story, but that depends on whether you have that much cash on hand and what you plan to do with it. Most people don’t have 35K available to buy a car outright.
I prefer cash over financing. Most buyers opt for the latter, and have been for decades.
The only difference is that, instead of immediately opting for a chip or retune, you now have the option to pay a monthly fee to keep your warranty.
2. BRK is dead even with the S&P 500 over the last decade. This is despite the fact that BRK has access to cheap/nearly free leverage
There are better examples out there if you want to critique EMH.
The original point still stands. The vast, vast majority of professional investors (let alone retail investors) underperform the market. Almost everyone who promises safe alpha is full of it.
Anything that involves a person one both ends of a transaction chain inherently has one or more 'human institution' dependencies.
As far as I'm aware, none of those involve anything that looks remotely like financial speculation.
It's almost impossible to beat the market after fees. Anyone who promises to do so, consistently, is full of it. French (2008) and a whole body of literature before and after.
We don't live in the states.
Dual incomes make this a weak metric. The Model 3 also retails for the same price as the BMW 5 series and Mercedes-Benz E class.
Tesla, pre Model 3, had real issues that went beyond panel gaps. Modern Tesla is doing much better, but they're competing against established players who differentiate through great interiors and "the little details".
Large portions of this customer segment are willing to trade long term reliability for perceived build quality and overall luxury. Tesla's value proposition, which is great performance and long term reliability at the expense of panel gap size and cheap leather, simply doesn't fit their expectations.
I went through something similar through the end of high school. I did, however, go through public school for elementary.
My experiences mirror a lot of what others on here have already written. The transition to back to traditional education (university) wasn't an issue.
If you are already motivated, going from a low to moderately more rigorous style of learning is easy.
The issue is going in the other direction, which is why so many first year university students fail.
The actual assets are held by a separate custodian.
The overview for the actual data product is at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurem...
There are a couple of different VNP46 products, but A2 is iirc usually the one used in academic research.
You can also use nighttime light data to learn about levels and patterns of urban growth. Compare VIIRS imagery for India in 2013 with 2022.
The issue is whether there's a strong correlation between GDP and nighttime light data in all regions. There's a weak correlation (a lot has been published on this) and it's a good tool if you want to identify sharp, short term changes one way or the other. It's arguable, though, whether it's an appropriate proxy for a lot of different regions/countries with different economic models.
GDP estimates from nighttime light data are possible but often inaccurate. Before you even get into the larger issues, there are a lot of small things, such as cloud coverage, ground coverage changes, lunar illumination, &c... that have to be accounted for.
It's great when you're trying to track urban development. It's good when you need quick ground truth estimates (e.g., you're looking at an area impacted by war or lockdown and you want to get a rough guess of the actual impact), and it's ok when you are looking at a country with an economic model (manufacturing/export-led) where changes in nighttime light volumes can tell you something.
It's iffy if you're trying to compare a lot of different countries, with different economic models, across different time spans, and you don't have the time to accurately assess whether nighttime light data is a good proxy for GDP in each of the many regions/country <-> economic model combinations you're looking at.