How to change timezone settings on Gmail, Google Calendar etc.

Every time I go back and forth between the East and West coast of the US, I have to google how to change timezones on Google products, because (1) the settings are hard to find, and (2) you have to do no fewer than SIX different things to avoid all sorts of weird timezone-related glitches:
  1. Update the timezone setting for your computer's system clock -- this is important because SOME but not all Google products use the system timezone (e.g. Gmail reads the system time zone, as do some but not all features in Google Calendar). The option to change the timezone can be found by right-clicking on the clock in the system tray and choosing a Preferences or Time/Date Settings option or similar. (In Fedora Linux, you can also type "system-config-date" as the superuser in a console.) Make sure "System clock uses UTC" is checked so that Daylight Savings time is handled correctly.
  2. Make sure you change the time zone on ALL devices that are logged into your Google account (phones, tablets, laptops, desktops etc.), or you'll have problems. Every time a device refreshes the Gmail webview, for example, it changes the displayed Gmail time zone for all connected clients -- so if you forget you're logged into your Gmail account on a second laptop, and that laptop's timezone settings are wrong, you'll have ongoing issues with multiple devices fighting to change the timezone back and forth between two settings.
  3. Restart Chrome (or whichever browser you use) -- Chrome doesn't always pick up the timezone change of your system clock until it has restarted, even if you refresh or log out of your Google accounts and log back in with the browser still open -- i.e. Gmail may continue to display message timestamps in the old timezone until the browser is restarted, regardless of the timezone setting on your Google account.
  4. In Google Calendar, you have to manually change the display timezone. Go to the gear menu near top right, choose Settings, then under the default tab (General), change the setting "Your current time zone".
  5. If you use Google Voice, it has its own timezone setting too: Go to the gear menu at top right, choose Settings, click the Account tab, and change the Time Zone setting there.
  6. Google Drive now has its own timezone setting too, although it is unset by default (which, I assume, means it uses the system timezone? Or maybe the Google account timezone, described below?): Go to the gear menu at top right, choose Settings, and in the General tab you'll see Time Zone. (I guess if it's unset, leave it unset, hopefully it will use one of the other correctly-set time zones.)
  7. (The really hard one to find): Go to https://security.google.com/settings/security/contactinfo , and under "Other emails", click Edit, which will probably take you to the more obscure-looking URL https://accounts.google.com/b/0/EditUserInfo?hl=en . (You can go directly to that second link, but it looks like the sort of URL that won't be around forever, so I provided the navigation directions.) Here you'll find a timezone setting that, if wrong, can mess with timezone display in strange places in Google products (e.g. the red line showing current time on Google calendar may appear at the wrong time, or events are shown at the wrong time, or popup reminders pop up at the wrong time, but not all of these -- I forget exactly what weird effects were caused by this problem exactly). This setting says it's optional, so you can probably select the blank line from the dropdown list, effectively unsetting an account-specific timezone. I'm hoping then that one of the two timezone settings above will be used instead, but I need to test this. 
(Probably Google apps should all read the browser's timezone, and the browser should watch for updates of the system clock and timezone. Hopefully this gets fixed someday...)

I hope this saves somebody some frustration!


On the nouveaux érudits and anti-theism

Since the rise of a few prominent and vocal atheists about five years ago, public anti-theist sentiment has increased dramatically. The nouveaux érudits are successfully introducing into the public subconscious the idea that theism as a liability. These days, everybody I know that believes in God in some way goes about it relatively quietly. The public scoffing at religion is unfortunate, because removing religion from public discourse robs humanity of an important aspect of human cultural identity and diversity. Just like in the natural world, diversity is at the root of the beauty of humanity.

Atheism is as much a belief system as any religion I can think of. Both theism and atheism is a belief and a choice. Unfortunately neither theism nor atheism constitutes a falsifiable hypothesis based on tangible, observable evidence, so we should all simply agree to coexist happily while respecting others' beliefs, since arguing, belittling, attacking etc. doesn't actually do anybody any good. I don't understand how much effort some people put into tearing down others' beliefs.

That applies to both theists and atheists, incidentally -- there is no justification for, or usefulness in, fighting with a person or trying to coerce them to believe the way you do in the name of either "saving their soul" or "protecting them from religion". Share what you're excited about by all means, if somebody wants to listen, but respect people's agency, and once they have heard what you claim as fact, let them figure out for themselves whether or not what you are saying is true. And you can teach people what you believe without tearing down what they believe. Or as the Qu'ran rather ironically says, "Let there be no compulsion in religion -- truth stands out clear from error."

Back to my original point though -- it is sad that the religious people I know generally don't feel that they can present this as part of their public identity anymore.

(In response to Nils Hitze's post on Google+ wondering why most people he follows are atheist. The above is also posted to my G+ feed, so there may be comments there too.)


Results before Methods??

Why do so many journals require you to put results before methods? That's so backwards, and it is maddening to have to write a paper to conform to. You can't explain what you found before you explain how you found it. This also betrays the mindset of many biologists that I know at least (since this pattern is ubiquitous in biology and biomedical journals), that you can safely ignore the contextual assumptions embedded in the methods when you interpret the results.


On leaving academia, and wanting to create "Google X" without the Google part

[ tl;dr version: I'm leaving academia after many years; have big life decisions to make; need a dose of perspective. What's a good next step? What's the likelihood of success of creating some sort of standalone Google X type lab with a few brilliant people and getting it funded in the current climate? ]

So I'm finally biting the bullet and leaving academia, after a nasty realization that the powers that be (the hand that was supposed to feed me, NSF, cough) -- in conversations right to the top of the hierarchy -- have insufficient technical understanding to tell sound ideas from the rest, and insufficient foresight to take a risk on funding potentially revolutionary ideas when there's an evolutionary idea from a good friend of theirs also submitted in the same round of proposals that lets them check off their keyword boxes.

As a result, I have started gingerly digging through some job listings on both coasts. When you've been in academia as long as I have (and had it drummed into you that "you will never amount to anything in this world, or accomplish anything much, if you leave academia -- and you'll never come back if you leave"), it's hard to look forward to being a code monkey. I have, however, worked as a software engineer several times, for several different companies, so it's not like I have never had a "real job", but I figured I should ask for a dose of perspective. Tell me that academia has been lying to me all these years :-)

Really, I know that the perception that academia is the be-all and end-all of innovation and world-saving is for the most part false or at least myopic, and nobody outside academia really sees it that way, even though some cool research comes out of all the major institutions each year. Academia is certainly not the be-all and end-all of wealth creation. Peter Diamandis (whom I know from Singularity University) once expressed complete disbelief that I would even be considering a career in academia -- "You'll amount to 1/10th of your potential if you stay in academia."). Deep down I know he's right, even if I still have a strong urge to at least keep my foot in the door.

Anyway, I also have a strong entrepreneurial streak (I have a doc I have been compiling over the last few years, that now consists of hundreds of different ideas, some of which might even succeed to some degree if the execution is good) but I also have a strong dislike for business operations (although I have been involved in a small startup before). There seems to be a lot of funding out there right now, and a resurgence of new ideas, as well as more risk-taking than the industry has seen for a decade or more. I think this might be the right time to try to jump into entrepreneurship rather than working for the man.

So, I guess the questions I have for anybody that stumbles across this post are:

(1) Does anybody have experience with leaving academia and getting back into it after having accomplished something useful in industry? Is it even worth trying to keep that option open if my greatest interests are all heavily research-oriented (and when I would go crazy with mundane coding), or are there all-round better alternatives? Does anybody have experience working somewhere like PARC or one of the Intel research labs?

(2) How hard would it be to create a new research lab with a few top-notch guys, as a standalone "skunkworks" type lab but based outside of any organization or company with deep pockets, and get it funded today? (i.e. something like the Google X lab, but run outside of Google -- this would obviously be a big investment risk if the focus is even partly on blue-skies projects.)  Is working with the type of employee that is frequently attracted to that sort of working environment (i.e. top 1% of engineers, theorists, inventive types etc. with all the crazy ideas and the audacity to think that they might be able to make them work) like herding cats? Has anybody worked at Google X or in a similar moonshot research lab?

I guess I'm just trying to figure out where I can make the biggest difference. Or maybe even thinking that way is a delusional after-effect of standing on the Ivory Tower :-)

PS in case anybody is hiring...  http://resume.lukehutch.com/


The Multicore Dilemma (in the big data era) is worse than you think

The arrival of the big data era almost exactly coincided with a plateau in per-core CPU speeds in 2004 and the beginning of the multicore era, and yet, the National Research Council found as recently as 2010, "There is no known alternative for sustaining growth in computing performance; however, no compelling programming paradigms for general parallel systems have yet emerged." [Fuller & Millett (Eds.), The Future of Computing Performance: Game Over or Next Level? Committee on Sustaining Growth in Computing Performance, National Research Council, The National Academies Press, 2011]. Additionally, as every field of scientific research and every major business endeavor begins to generate more data than can be easily analyzed on a single CPU, it has become evident that this "multicore dilemma" is impeding progress across all of science, and across all fields of business. The multicore dilemma, therefore, presents the biggest near-term challenge to progress.
Processor performance from 1986 to 2008 as measured by the benchmark suite SPECint2000 and consensus targets from the International Technology Roadmap for Semiconductors for 2009 to 2020. The vertical scale is logarithmic, where a straight line trend indicates exponential growth. A sharp change in the growth rate can be seen around 2004. Before 2004, processor performance was growing by a factor of about 100 per decade; since 2004, processor performance (in GHz) has been growing and is forecasted to grow by a factor of only about 2 per decade. Source: [Fuller & Millett 2011]

The coining of the term "big data" is a reflection of the fact that the exponential increase in data generated per year, which has been going on for many decades, finally crossed some scale where humans sat up and started paying attention. This phenomenon occurred across a wide range of fields over a short period of time, including at the leading edge of most fields of scientific research, where many scientists (who have no training in parallel and distributed systems) are now having to spend a significant proportion of their time trying to figure out how to parallelize their analysis code. The difficulty of parallelizing code is impeding progress in all major fields of science, and the seriousness of this problem cannot be overstated.

DNA Sequencing costs: Data from the NHGRI Large-Scale Genome Sequencing Program

Ultimately, no matter what kinds of parallel computing paradigms may be created, if they require conscious engineering to use, they will incur cognitive overhead for programmers, and significant programmer knowledge and expertise, which is not possessed by the large number of non-skilled programmers (e.g. medical researchers) who must write parallel code just to get their job done. Long-term, the only sustainable approach to parallelization is the one that requires close to zero programmer effort: “Write once, parallelize anywhere”.

Figuring out what to parallelize, and how to parallelize it, is not a problem we should be assigning to humans to solve. The lead authors of the NRC report further observe that “The intellectual keystone of this endeavor is rethinking programming models so that programmers can express application parallelism naturally. This will let parallel software be developed for diverse systems rather than specific configurations, and let system software deal with balancing computation and minimizing communication among multiple computational units.” [Fuller & Millett 2011] Without finding a better model for automatic parallelization, some great challenges lie ahead in the big data era.

The multicore dilemma meets big data: incidental casualties

There are several important ramifications of the confluence of "the end of Moore's Law" (as it is traditionally interpreted) and the beginning of the big data era:

  1. We are facing an impending software train-wreck. Concurrency introduces whole new classes of potential bugs, and the human brain is notoriously bad at writing safe parallel code. As per-core speeds plateau, we are not just headed towards a plateau in software speed, but an era of significantly buggier software.
  2. We are facing an impending software morass. Parallel code, using current parallelization paradigms, is not just harder to write, it is more complex, and therefore harder to understand. It is very easy to get mired in a spaghetti of parallel code. All current platforms, libraries and techniques for parallel processing introduce significant issues:
  1. Parallel programming requires additional programmer effort. The data dependencies inherent in a problem must be determined manually by a programmer in order to design a parallelization strategy, and the implementation must be shoehorned into some parallelization paradigm that is appropriate for a specific target architecture.
  2. Parallel programming makes debugging, maintaining and extending code significantly more difficult. Parallel programming produces boilerplate code, increases the likelihood of introducing hard-to-trace bugs, and increases the complexity of program structure.
  3. Parallel programming can result in longer development times. For large-scale projects, reining in the added complexity of parallelization can be intractable, either leading to the failure of the project or the abandoning of parallelization.
  4. Parallel programming can incur significant extra cost. Programmers who have a solid grasp of parallel programming are rare and expensive.
  1. We urgently need automatic parallelization for “mere mortal” programmers. We urgently need a programming language for the masses that does not require a background in concurrency or parallel and distributed systems. Increasingly, programmers of all skill levels, and from all fields, are being required to parallelize their code. Many programmers with little or no formal CS background (e.g. biomedical researchers) are writing code to target parallel architectures, for example using a series of duct-taped Bash scripts to launch R and MatLab jobs in a work queue on an NIH-funded compute cluster. It will soon no longer be possible to do biology without doing computational biology, or physics without doing computational physics, and dataset sizes in many fields of science long ago outstripped what may be quickly computed on one processor. The NRC report gave the following advice: "Recommendation: Invest in research and development of programming methods that will enable efficient use of parallel systems not only by parallel systems experts but also by typical programmers" [Fuller & Millett 2011]
  1. After the big data era will be “the messy data era”. Data types are becoming increasingly diverse, increasingly varied in numbers and types of attributes, increasingly sparse, increasingly cross-linked with other disparate datasets, and increasingly social in nature. We need a smarter compiler that will automatically parallelize our code and then get out of the way, relieving us of the cognitive burden of figuring out how to parallelize the code, and allowing us to focus on the more important issues of the specific problem we are trying to gain insight into.

The solution to the multicore dilemma: “the sufficiently smart compiler”

It is clear that the multicore dilemma is one of the most pressing issues directly or indirectly impacting all of scientific research today, and that a robust solution must be found as quickly as possible. Paul Graham recently wrote:

"Bring Back Moore's Law: The last 10 years have reminded us what Moore's Law actually says. Till about 2002 you could safely misinterpret it as promising that clock speeds would double every 18 months. Actually what it says is that circuit densities will double every 18 months. It used to seem pedantic to point that out. Not any more. Intel can no longer give us faster CPUs, just more of them.

"This Moore's Law is not as good as the old one. Moore's Law used to mean that if your software was slow, all you had to do was wait, and the inexorable progress of hardware would solve your problems. Now if your software is slow you have to rewrite it to do more things in parallel, which is a lot more work than waiting.

"It would be great if a startup could give us something of the old Moore's Law back, by writing software that could make a large number of CPUs look to the developer like one very fast CPU. There are several ways to approach this problem. The most ambitious is to try to do it automatically: to write a compiler that will parallelize our code for us. There's a name for this compiler, the sufficiently smart compiler, and it is a byword for impossibility. But is it really impossible? Is there no configuration of the bits in memory of a present day computer that is this compiler? If you really think so, you should try to prove it, because that would be an interesting result. And if it's not impossible but simply very hard, it might be worth trying to write it. The expected value would be high even if the chance of succeeding was low."

[Paul Graham, Frighteningly Ambitious Startup Ideas #6, March 2012]

It has furthermore been observed that “exploiting large-scale parallel hardware will be essential for improving an application’s performance or its capabilities in terms of execution speed and power consumption. The challenge for compiler research is how to enable the exploitation of the [processing] power of the target machine, including its parallelism, without undue programmer effort.” [Mary Hall, David Padua, and Keshav Pingali, 2009, Compiler research: The next 50 years, Communications of the AC 52(2): 60-67.]

The “sufficiently smart compiler” cannot actually exist (for modern imperative programming languages)

Functional programming languages are implicitly parallel (automatically parallelizable), because calls to pure functions have no side effects. However, “mere mortal” programmers are not able to be productive in functional programming languages, or are unwilling to use them.

Many efforts to automatically parallelize imperative programming languages have been attempted with varying success, but with imperative languages, the compiler must be overly cautious in applying parallelization strategies, because it is not just difficult but actually uncomputable in the general case to safely parallelize imperative code, because the data dependency graph of an imperative program (the graph of which values are used to compute which other values) can’t be known until you actually run the code. Static analyzers can often only guess at the origin or identity of a specific value without actually running the code. The sufficiently smart compiler cannot actually exist for modern imperative programming languages.

We need a new programming model that allows programmers to work in the maximal implicitly parallel subset of the imperative paradigm while allowing the compiler to safely automatically parallelize the code.


Darwin was (half) wrong

Slashdot ran a story, "The science of handedness". I'm pretty sick of reading this sort of thoughtlessness describing evolutionary biases. If you're going to say that an adaptation gives a reproductive or predatory advantage, then fine, you're talking Darwinian evolution -- survival of the fittest -- and that's pretty trivially easy to show, even in a lab. If you're going to say, "Everybody in chummy societies had the same handedness so they could share tools", then please tell me how that weak-sauce tiny (or effectively zero-magnitude) biological fitness bias is supposed to have produced a genotypic change to an entire species within the known anthropological lifetime of the species. Remember that Darwinism requires that for your random trait variation to survive and thrive, at a minimum you have to pass your genes on while somebody else does not. So, I have to wonder if the authors "believe in" Darwinism or if they do not.

This gets at what I think is a much bigger issue: fundamental to Darwinism is not just survival of the fittest, but also randomness. I think that true biological evolution -- what's actually happening in the real world -- is not Darwinism, because it is very non-random. It is inconceivable that the complexities of the human organism, or any other for that matter, could have occurred by chance via a random walk through the state-space of possible genetic mutations (many of which could easily give rise to non-viability) in the number of generations since the major forks in the tree of life. There just isn't enough time, enough generations. There isn't sufficient evidence of non-viability, through miscarriage etc., for the worst mutations to die out -- and there isn't sufficient evidence that most traits that are said to evolve through "survival of the fittest" actually gave the possessor of that attribute an actual survival advantage, a reproductive advantage or an advantage as a predator, at the expense of those that did not possess that attribute.

What's the alternative? Even setting religious issues completely aside, personally I think that built into every feed-forward mechanism in biology, crossing back across vast numbers of levels of emergent complexity, are corresponding feed-BACK mechanisms (actually, back-propagation mechanisms, to use the machine learning terminology) such that a system's biology -- and even its genome -- can "learn" in response to environmental stimuli. Everything we have come to understand about learning and optimization from the field of machine learning supports the hypothesis that to learn anything, at any appreciable rate, you must introduce feedback loops that back-propagate the error between expected and observed in some way such that the model can be updated to reduce the error for future predictions. In other words, mutations (and epigenetics) are very NON-random, driven by the environment and by life-experiences and even by the conscious choices of the host organism. This is much less about Lamarckism (although epigenetics are proving Lamarck was pretty much right) and much more about *directed* evolution (i.e. evolution being a biological learning and optimization problem).

In summary, I claim that Darwin was (half) wrong: evolution is about fitness, but optimizing for a given fitness function is not necessarily a random walk.


How to legally work in the US as a student

Somebody asked me how to gain work authorization in the US as a student. I have lived here for 11 years on student (F-1) and working (H1-B) visas. Here is the quick summary I sent back about what I have learned:


During your time as a student on an F-1 visa, you can work 20 hours a week but it has to be on-campus. The best sort of job is a research assistantship or teaching assistantship, since the college will often also pay your tuition.

As far as working off-campus: the easiest thing is to apply for one-year CPT (Curricular Practical Training) during your degree, and/or one-year OPT (Optional Practical Training) after graduation. If you are doing a STEM degree (Science, Technology, Engineering, Mathematics), then you can apply for a 17-month extension once your 12-month OPT is finished. CPT and OPT give you an actual EAD (Employment Authorization Document - a card) which gives you employment authorization, so now you can work more than 20 hours a week and you can work off-campus. You can even work multiple jobs, one or more of which can be self-employment.

After OPT, the easiest thing to do is get an H1B to work at a specific company. (Or marry a US citizen.) Generally a company will sponsor you for two 3-year H1B visas before sponsoring you for a green card. You can't apply for a green card directly from an F-1 visa, it is a "non-immigrant intent" visa. You can apply directly from an H1B, it is a "dual-intent visa".

Another option is to apply for an O-1 visa ("alien of extraordinary abilities") if you have some major award or accomplishment that is equivalent to national recognition in the US. However an O-1 has non-immigrant intent. If you ever want a green card, the better option is an EB-1 visa, which is dual-intent. You can then apply for a green card on the National Interest Waiver program, which allows extraordinary aliens to get fast-tracked to a green card if you can prove that it is in the United States' interest to do so. The NIW qualification can be proved by a string of high-profile publications in top journals, among other things.

There's also the green card "diversity lottery", which you should apply for every single year, since it doesn't actually count against you as an attempt to gain citizenship when they ask you at the border if you have ever sought citizenship on your F-1 "non-immigrant intent" visa (you don't have to declare dv lottery attempts): https://www.dvlottery.state.gov/ (Beware of all other sites than this one, some sites will charge you $10 to apply, this one is free and is the official site.) Applications open later in the year. Your chances at getting a diversity lottery green card range from quite high to vanishingly small depending on what country you are from.

There is also a startup founder visa that has been proposed, and the bill is, going through the system right now. You have to employ a certain number of Americans, and bring in a certain amount of funding within the first 1-2 year period for the visa to get renewed.

Here is info on the startup visa act. Everybody should consider supporting this.


The last link also talks about the possibility of working on an EB visas. Notably, EB-5 is a category for people investing a lot of money in the US. This can get you in the door if you can afford it.


Update: JBQ posted the following comments on my G+ post that links to this blog post:

Chances are, if you'd qualify for an O-1, you'd also be EB1 in the green card process and that'd probably be a very easy path.

Also, there's no benefit in waiting to apply for a green card when starting a new H-1B job. It just delays the priority date and therefore the waiting time.

Finally, the total experience doesn't matter when converting an H-1B to a green card, what matters is the experience when getting hired. Switching companies can be beneficial, as there can be enough experience to move from EB3 to EB2, while keeping the priority date. It's best to do that with more than a year left before the 6-year line, and it's best to do that with a priority date far enough in the past.

IIRC there are no specific requirements to apply for a green card.

As it was for me, the process starts with a certification by DOLETA that there are no citizens or residents to fill the job (similar to an H-1B, but a bit more thorough). Applying for this also sets the priority date.

Once that's done (a few weeks), the next step is to apply for the visa petition (proving that the employee is qualified and that the employer can pay them). That's also similar to an H-1B. That can take a few months IIRC. This is the I-140. Don't wait long as the labor certification is only valid for a limited period.

Once that's done, the next step is to wait for an available visa. There are 3 waiting lists per country of citizenship, based on the skill level. The waiting lists are shorter for the categories with the highest skills. Each list is represented by a cutoff date, and if your priority date is earlier than the cutoff date for the category you're in you're eligible for the next step.

Finally, once a spot is available, applying for an I-485 adjustment of status turns the H-1B into a green card. That also takes a few months IIRC.


Intolerance: criticizing what somebody *is*, not they ideas they believe in

Somebody just asked the following question to the csail-related mailing list about the appropriateness of political slurs during public talks:

On Wed, Mar 14, 2012 at 9:19 PM, IJ wrote:
I was wondering what people in CSAIL think about speakers including gratuitous political insults in their talks.

I was at a talk last week at HMS about systems-based analysis of disease. The speaker, Joseph Loscalzo (Chairman of Medicine at the Brigham), said that before Hippocrates people thought illness was caused by evil spirits. He then added that this view is shared by Republicans.

Coming from a background in industry where one often encounters very nice, very intelligent people of all political leanings, I found it shocking that the speaker would be so unprofessional as to insult people who, for whatever reason, have a political affiliation different from his own. Still worse was his subsequent joke that there might "even" be some Republicans in the audience, with its presumption that all or almost all of his audience must share his political views. I thought he would next suggest that if we spot one of these Republicans we might examine him or her as an interesting specimen!

As discussed in this New York Times article, and borne out by my own acquaintances, academia can be a hostile environment to people who are not liberal Democrats (www.nytimes.com/2011/02/08/science/08tier.html). The researcher mentions how these non-liberals remind him of closeted gay students in the 1980's, how they "hid their feelings when colleagues made political small talk and jokes predicated on the assumption that everyone was a liberal." I know of medical students who were justifiably afraid that they would be discriminated against if their political affiliations were "outed".

I hope that in CSAIL we would not tolerate remarks like these that create a hostile environment for any of our members, students, or guests, whether they are women, gays, or even Republicans.

Does CSAIL have a policy on this?

Should it?

RMS jumped in with the following:

On Thu, Mar 15, 2012 at 12:22 AM, Richard Stallman wrote:
People are responsible for their opinions; criticizing and even
condemning political opinions is a normal part of political discourse.

Here's my take on it:

There is a difference between criticizing a theory that somebody holds, and something that a person *is*. The former is the foundation of academic discourse, and theories must be able to withstand scrutiny to be of value. The latter -- criticizing what somebody is, and either hasn't chosen to be (in the case of phenotypic attributes -- skin color, gender etc.) or has chosen to be, by culture or agency (religion, political orientation etc.), is intolerance. In the context of the original poster's situation, it is OK to criticize a theory about how the government should be run, and to subject that theory to academic discourse about relative merits or lack thereof. It's not OK to poke fun at somebody's *identity* as a person that follows a given ideology ("one of those people", in label-speak).

I really don't like the word "intolerance" however: the grand irony is that much of the time that this word is used, it is used to superciliously indicate that another person's views are quaint, and not broad enough to include one's own views. Whether or not that is true, unless one party is being harmed, tolerance must be extended in both directions, or a claim of intolerance is plain hypocrisy.

The word "intolerance" seems to have therefore lost a lot of its real meaning, because it is often used in this self-serving way. Is there a less worn-out word available for use than "intolerance"? (I guess this is why policies about this generally refer to harassment, not intolerance, because they deal with cases where one party is in fact harmed?)

Stephen Wolfram, Quantified Self and LifeScope

Stephen Wolfram's recent blog post that presents a visualization of a lot of his life data is opening a lot of eyes. I submitted an app to the original Android Developer Challenge back in 2008 that produced many of the same exact data plots. I called it LifeScope. It contains its own custom graph plotting library and a completely generic data handling backend that you should be able to plug a range of different data sources into.

Stephen Wolfram's blog post has inspired me to clean up my code and get the app out. Please leave a comment if you would find this app useful, and let me know what data sources in your life you would be interested in plotting.


On life, death and so-called "brain death"

Cross-posted from my reply to the following TED Conversation: "How does life/death manifest itself in the human brain? Is brain death the ultimate end stage of life?"

There is something to the fact that, to maintain a living state, the brain requires a pattern of oscillatory activity with the power distributed in certain frequency bands according to the type of activity that the brain is engaging in. (See Rhythms of the Brain by G. Buzsáki.) However, even after a massive epileptic seizure, which typically indicates a widespread state of electrical noise, the brain is usually able to recover these baseline rhythms.

I think in persistent vegetative state, the brain has very little normal electrical activity, but still, the activity is non-zero -- and the brain appears able to wake itself in some cases. There are stories of people waking up from PVS after several years. It's also curious that you can keep a person's body alive for a long time after their brain is declared "dead" as long as you keep blood flowing and oxygen and nutrients at the right levels. Personally I think that implies the organism couldn't really be declared dead to start with. I don't think it's possible to accurately declare an organism dead until rigor mortis sets in and its microbiome begins to consume it -- in my opinion, decay and the succumbing to entropy is the only true sign of death -- and these forces are set in motion very quickly once an organism "actually dies".

Note that recent research has shown that administering an intravenous dose of Ritalin to a comatose mouse can cause the mouse to wake up almost instantly. They have yet to start human trials, but this may hold real hope for "rebooting the brain".

How long should we keep a PVS patient alive for though? Is it worth 20 years of stress on the family and untold cost of life support? I don't know, but I would say that we need a better understanding of the types of baseline electrical situations from which the brain is able to reboot before we can authoritatively say we know that a patient is actually "brain dead", i.e. beyond the chance of recovery.


KNIME -- the Swiss-army knife of data workflow

I just discovered the following Swiss-army knife for handling data workflow. I have wanted something like this to exist for years, and was getting close to starting my own project with almost identical goals, but I guess I don't have to now:

KNIME lets you build a data analysis pipeline, complete with data normalization and filtering, inference/classification and visualization steps. It caches data at each node in the workflow (so changes to the pipeline only result in the minimum necessary recalculation), and keeps track of which experimental variables produced which results. It intelligently makes use of multiple cores on your machine wherever possible. It incorporates the entire Weka machine learning framework. It lets you add your own visualizers for different data types. It cross-links the highlighting of data points between different tables and views, so that if you select a data point in one view, it selects it in all other views. It reads and writes a large number of different data formats and can read from / write to a live database. You can call out to R at any point if you have existing R code you need to run on a piece of data.

i.e. KNIME basically does everything that anybody who works with data does every day, and keeps everything tied together in a nice workflow framework with built-in data visualization, smart caching, smart parallel job launching etc. etc.