Engineers rarely need to travel anywhere, whereas sales people need to be on the road all the time working with and at customers, even in technical (e.g. "sales engineering") roles. Travel is very costly, when I was in sales engineering doing on-site proof of concept deployments, demonstrations, etc... I was easily racking up travel expenses equal to or greater than my annual salary. And this wasn't particularly glamourous travel; customer sites where the technical guys are tend to be out in the middle of nowhere. As a ballpark, that $250K number you cite would be enough to support around 3-10 sales people depending on how on-site intensive your product and sales model is. I presume you know how many engineers you have, so you can compare and decide for yourself.
I can make pixel perfect feature rich cross-platform native application for Linux, Win, BSD, OSX, Android, iOS in 1/3rd the time it takes me to ensure the same "web app" works in all the browsers and OSs.
I want whatever development tool chain you're using. Just dealing with the different installer mechanisms on those platforms makes my head spin. What's your secret?
You'll see these kinds of large-scale columnar stores like Cassandra or HBase being used a lot in metrics and log management projects.
For instance, if you want to generate a histogram of login processing time over the last 90 days, you'll need to record the times of all of your individual logins to do that. If you have millions of logins per hour, that single metric alone is going to generate a lot of rows. If you're also measuring many other points throughout your system, the data starts getting unmanageable with B-tree backed databases and not of high enough value to store in RAM.
in the past, you might deal with this by adding more sophisticated logic at the time of collection. Maybe I'll do random sampling and only pick 1 out of every 1000 transactions to store. But then, I might have a class of users I care about (e.g. users logging in from Syria compared to all users logging in around the world) where the sample frequency causes them to drop to zero. So then I have to do more complicated logic that will pick out 1 out of every 1000 transactions but with separate buckets for each country. But then every time your bucketing changes, you have to change the logic at all of the collection points. I can't always predict in advance what buckets I might need in the future.
With more log-structured data stores and map-reduce, it becomes more feasible to collect everything up front on cheaper infrastructure (e.g. even cheap SATA disks are blazingly fast for sequential access, which B-tree DBs don't take advantage of but log-oriented DBs like Cassandra as specifically architected to use). The data collected can have indexes (really more like inverted indexes, but that is a longer discussion) up front for quick query of data facets that you know you want in advance, but still retains the property of super-fast-insert-on-cheap-hardware so that you can store all of the raw data and come back for it later when there is something you didn't think of in advance, and map-reduce for the answer.
I'm not sure if it's a typo or a misunderstanding, but the statement in the summary about atomic batching is hilariously incorrect.
Atomic batching has nothing to do with "patches can be reapplied if one of them fails", but rather the more pedantic yet common case where you want a set of data updates to be batched atomically, where all or none of the changes occur, but nothing in between.
Take a look at some of the full ride scholarships in those links. You need far from 4.0 GPA or 2400 SATs. There are programs with GPA requirements as low as 3.0 and SATs at just the 1400's. Certainly above average, but by no means extraordinary...
The route described in the article is kind of arcane, and he leaves out one of the easiest ways, not just for getting partial funding, but even getting all of your costs funded: High SAT scores.
There are plenty of fully accredited 4-year universities out there who will pay for everything just based on SAT scores or a combination of GPA and SAT scores.
We're talking "Full Ride", like tuition, room, board, and books in many cases:
or significant scholarships that can get the net 4-year cost down to varying levels:
All based on quantitative measurements alone.
It's hard to say why Richard Linder went through such obscure means in order to get his credits rather than just studying his ass off for the SAT's, but I suspect the reason why he went for "cheap credits" is where the real untold story is.
JetPens is the ultimate source for Japanese writing instruments, including the Pentel Slicci:
Are IAPs really that transient on Android? I must admit that I don't currently own any Android devices, but on my iOS devices, in-app purchases apply at the iTunes account level and are not only persistent, but also apply (without any extra purchase) to all instances of that app across different devices set to the same account. The iOS behavior where purchases are tied to your account and not to any particular device has made buying both apps and upgrades much more appealing to me than I originally thought would be the case.
You bought a new car via a private sale? That's pretty interesting, do you have more information about how you did that and where your seller got the cars from?
Your #1-4 do certainly match my experience. Your point #5 though doesn't seem to be borne out by the facts.
The notion that engineering majors make less than finance and business majors isn't borne out by the statistics. Law is an unfair comparison since that's an additional 3 years of expensive professional degree tuition, although their new-graduate employment numbers aren't doing that great.
Let's compare stats. Here we have have an undergraduate business program, hyped as being in the top 20 undergraduate business programs (pay close attention to the mean base salary and % employment numbers):
Here we have an undergraduate engineering program, also hyped as being highly ranked, at the same university, for the same year:
Now, the business degree majors do have their data updated for 2011, the engineers are only at 2010, but take a look at the 8 year trend reports to satisfy yourself that the numbers are relatively stable:
Undergrad CS majors are making 28% more than the undergrad business majors. Electrical engineers are not doing as well as the CS majors, but still better than the business majors.
The majority of business majors end up in just as boring and dead-end jobs as the majority of other majors. You can't look at the high-flying business and finance guys on Wall Street and think that those guys are "typical" for business majors any more than you can look at Bill Gates, Gordon Moore, or any of a whole range of tech company CEOs and execs, and think that they are typical engineers.
If China were simply limiting the amount of rare earths permitted to be dug out of the ground, there would be no WTO issue. The problem is that this is an export cap which has the potential to create different pricing for rare-earths between domestic and foreign purchasers of these materials.
Now if you look at mentions of today's prices of rare earths (by googling for "rare earth prices"), as yet, there is no such disparity. The linked WTO article also doesn't directly talk about price disparities between domestic and foreign purchasers. It turns out that global demand for rare earths went down quite a bit last year, and as a result, only about 60% of the export quota was used up (according to this FT article).
The concern is that as the global economy recovers, if demand is seen to exceed the quota, then a huge price difference between what domestic companies and foreign companies pay will emerge. This would amount to a kind of state subsidy (making prices for domestic producers artificially cheap) and would violate WTO rules.
The two metrics to watch to determine whether or not the claim of environmental protection vs. economic protectionism would be:
(1) Domestic rare earth production volume (e.g. in tons) - If slope of this curve continues unchanged, then there really is no environmental effect. If the slope flattens out, then it could be argued that the quota did slow down the pace of mining and did have an environmental consequence
(2) Domestic (China buyer) vs. Foreign (non-China Buyer) price (e.g. difference $/ton) - If this disparity is big, then there's a stronger case that there is some kind of domestic subsidy occurring, if the disparity is small, then the case that there is a subsidy is weaker.
This is not really a matter of sovereignty since China is a willing party to the WTO and has volunteered to play by those rules.
What this is, is a recently declassified correspondence between John Nash and the NSA from January 1955. In it, John Nash makes the distinction between polynomial time and exponential time, conjectures that there are problems that -cannot- be solved faster than in exponential time, and uses this conjecture as the basis on which the security of a cryptosystem (of his own design) relies. He also anticipates that proving complexity lower bounds is a difficult mathematical problem. These letters predate even Godel's letter to Von Neumann, which goes into much less detail about complexity, and yet has also been taken to anticipate complexity theory and the P vs. NP problem.
Link to Original Source
Link to Original Source