I don't know about Nexus phones specifically, but unless the hardware is locked down you should be able to put Android in a chroot jail instead, with a proper GNU/Linux environment on the outside. Then in the Android world you can run a VNC client app, or ssh in a terminal app (with optional X server app), to break back out.

I got it working on my N900 substitute, a $200 "Samsung Galaxy S Relay 4G" (SGH-T699), which has a great 5 row slide out keyboard to make up for its otherwise unremarkable (but still better than N900) hardware. There are still a lot of rough edges to smooth out--currently to direct audio to the headphone port I use a shell script with 9 amixer commands, for example--but I get the sense that this is just a matter of putting in more time tinkering.

I think there is a significant number of women who are interested but are dissuaded from studying computer science for some other reason(s).

My school had an overall male/female ratio of something like 70/30, and most of my computer science classes were closer to 90/10. But "pure" math courses tended to be close to 50/50 as well as more racially diverse if I remember correctly. Courses cross-listed between both departments fell somewhere in between.

Shouldn't there have been a large overlap between the groups of people interested in, for example, complexity theory (computer science department) and graph theory (math department)? Besides covering related content, both courses emphasized a similar set of skills, primarily reading/writing formal proofs, and had similar difficulty and workload levels. Neither course was required for any major or had formal prerequisites. Yet complexity theory was dominated by white men and graph theory was not. Why is that?

I don't deny that there are fewer women overall who are interested in science and technology fields, but even among the women who are, in my experience a disproportionate number of women chose math over computer science. (Or perhaps it is the men who chose computer science over math?) Either way this suggests to me that the problem extends beyond level of interest in course content.

I think one solution to this is for us to remind them they are actually our public servants as often as possible. If you are lost, then go up and ask them for directions if they seem to be standing around doing nothing. Hell, maybe even ask them if you are not lost just so they get to talk to a law abiding citizen for a change. Then, if they are helpful, be polite and courteous and make sure you say thanks.

They will still have to deal with utter some scumbags, but maybe if they spent more time dealing with people who are not then they might find it easier to not treat everyone like they are.

I have done this several times (not for this purpose but because I needed directions or other minor assistance). Each time the officer seemed to genuinely appreciate the chance to help and be seen as the "good guy" in the eyes of the person he was interacting with.

Not that my experiences are any more than anecdotes, but they line up with your thoughts. How many police officers assume the worst of us because we assume the worst of them and treat them accordingly?

Fraudulent speech is still speech, yet even most libertarians agree with penalizing fraud. Commercial speech being speech doesn't automatically exempt it from regulation.

I'm not the grandparent poster, and my political and economic views are well to the left (within the US spectrum). But I, too, found that many candidates were astonishingly bad in a way that no amount of training would likely have corrected for.

At my previous job, our test/icebreaker was simple: we sat the candidate down at a computer with a C++ file to read and digest for up to half an hour, left the room and then returned with this question: "So, what does this code do?" There were no tricks--the file was about 300 lines long, was designed to be readable (with meaningful variable names and occasional comments), and used only the most basic of C++ features. It did nothing more sophisticated than applying some logical tests to an input object in order to select a return value.

You know what I did that impressed the team when I was asked "So, what does this code do" on my interview? I gave a one-sentence summary before diving into details. I was skeptical when my boss told me this shortly after starting the new job. But we interviewed a dozen or so additional candidates over the following year, and half of them were only capable of giving line-by-line explanations ("Well, at the top of this function first we check if this parameter is less than 60, if so we stop and return this value, otherwise...") even after we stopped them and specifically asked for a brief high level summary. They all interpreted the individual lines of code correctly, but only half could express what the code did as a whole.

We hired three people from this group (so about 1/4 of the people we interviewed). And we did provide plenty of training, or more accurately, we each taught each other based on our individual strengths. I had more SQL experience than the rest of the team, for example, so I took on the more difficult SQL-related tasks myself while others completed simpler tasks with my advice and guidance. Similarly, another member had more experience with pthreads than I did, so he helped me whenever I had issues related to multithreading. The end result was a team where everyone had specialties but could complete basic tasks in any relevant area.

Texas has the federal government to fall back on in case of, for example, natural disaster. The federal government doesn't have such a safety net; it must self-insure. On top of that, the federal government has to be prepared for contingencies such as war that do not really apply at the state level.

The period of time, one year, is arbitrary. Requiring a balanced yearly federal budget would be like requiring a balanced personal budget every two week pay period, even though my biggest expenses occur monthly.

What we really need is some way to balance the federal budget over a much longer period of time, a decade or two perhaps, spanning a full boom/bust cycle. This is, of course, much easier said than done.

Or you can press Ctrl+Shift+Del. One of the options (which should already be checked if you used it last time) is to clear the cache. A three-key combination and a button click and you're done, with no plugins needed.

No subsidy. Household of 1, 32 years old, income high enough that there is no subsidy (put 100k or whatever). A silver plan is estimated at $272/month.

Data points for comparison (non-smoker, no pre-existing conditions):
* 2005: Employer health plan similar to a silver plan. Monthly premium was $330/month of which the employer paid 75%. (Compare $231/month from the calculator for a 25 year old.)
* 2007: Unemployed. The cheapest individual plan I could find was $500/month, similar to a silver plan; COBRA let me keep the $330/month plan but I had to pay 100% of the premium. $150/month state-subsidized option would have been available but only after I depleted my assets. (Compare $241/month from the calculator for a 27 year old.)
* 2010: Employer health plan similar to a gold plan. Monthly premium was about $600/month of which the employer paid 75%. (Calculator doesn't give numbers for a gold plan.)
* 2012: Employer high deductible health plan similar to a silver plan, with the employer paying 100% of both premium and deductible used. Monthly premium was $350/month, and will remain unchanged through 2014. (Compare $272/month from the calculator for a 32 year old.)

Now, I live in New York rather than California, so I wouldn't have the rates given by California's calculator. Instead it looks like the cheapest silver plan where I live will be $300/month.

And the amount California's insurance cost calculator shows me is $80/month cheaper than what my employer currently pays for me, and over $200/month cheaper than the cheapest plan I could find as an individual when I was unemployed over 5 years ago.

How is the government subsidizing coverage for its employees any different from private sector employers paying some or all of their employees' premiums as a job benefit?

They could simply ask--yes or no--on the tax return, then require people who are audited to bring paperwork backing it up (just like any other claim on a tax return).

How would property be divided up at the year-end "divorce" of college roommates, especially if they end up not getting along? That alone might be messy enough to discourage such sham marriages, I think.

I can't think of the last time I've heard of a Justice saying that he personally detests the ruling but 'this is what the law says'

Roberts on the constitutionality of the ACA, perhaps?

The Affordable Care Act’s requirement that certain individuals pay a financial penalty for not obtaining health insurance may reasonably be characterized as a tax. Because the Constitution permits such a tax, it is not our role to forbid it, or to pass upon its wisdom or fairness.

This reads to me as "I don't like it, but my hands are tied". Why mention the wisdom or fairness of the law, rather than stopping at "it is not our role to forbid it", if he thought the law was wise and fair?

The people I've talked to generally prefer "black". The key is to use it as an adjective rather than as a noun: "black" describes them, but doesn't define them. "Black people": good. "Blacks": not so good (though better than some of the alternatives).
I imagine I'd feel the same about being defined rather than described by any of my physical traits.

And this is the kind of thing an introductory philosophy of science course would cover. What are the fundamental (and typically unstated) assumptions we make about the universe in order for science to be useful, and what would the implications be if any of these assumptions were false? What are the limitations of measurement? What kind of questions can and can't be answered scientifically? What is the relationship between math and science?

Add in some formal logic and basic statistics, and students will have a better understanding of the levels of certainty in science and how to identify the assumptions to be reexamined when experimental results differ from the expectations that follow from those assumptions.