Archive for the ‘Research’ Category

A computational person’s nightmare

April 21, 2013

Anybody who writes code for her research knows about the scary feeling of a bug being there in the code; may be the exciting new results are all but an artifact of the code; and, what is worse, may be the bug in the code is a silly, elementary mistake.

Of course, I have had my share of such nightmares too — not just the feeling, but at least twice, did notice mistakes themselves. But, fortunately for me, (a) I noticed them myself; and, (b) the mistakes were minor.

All this has taught me that while it is almost impossible to avoid bugs in codes, there are several ways in which one can pick them up: (i) Have some sharp eyed colleagues go over your results (and, if you are as lucky as me, they might even be willing to go over your code) and spot any unphysical results; (ii) Explain you code to a human; this is almost a fail-safe way of spotting mistakes; I think in the coding community this is closer to the pair-programming concept; (iii) Keep benchmarking your code and keep coming up with newer and newer tests; while bechmarking, I have also found that it is important that you match numbers within the accuracy of your calculations; numbers not matching at the fourth or fifth decimal place, sometimes have led me to identify the bugs in the code. (iv) Make your code open source and share with as many users as possible. Even if a few of those users turn out to be developers, they will notice errors, if any. (v) If possible, find another colleague who will implement the same code, and results matching from two people increases the confidence in the code enormously; as my advisor used to say the probability of two people making the same error goes down multiplicatively.

All the above thoughts are triggered by this link list from Abi, and especially this report.

Of course, if you are any lucky, you will also get to experience, once in a while, the following feeling during your research career:

“I almost didn’t believe my eyes when I saw just the basic spreadsheet error,” said Herndon, 28. “I was like, am I just looking at this wrong? There has to be some other explanation. So I asked my girlfriend, ‘Am I seeing this wrong?'”

I will save my thoughts on this for another post (and also my thoughts on the ease with which you can mess up things while using excel: I remember the terrible mess I made while grading for the first time for a large class using excel spreadsheet). In the meanwhile, I recommend that you follow all the links in Abi’s post.

 

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Annals of life-changing admission mistakes

October 16, 2012

We have heard about Verghese Kurien’s switch to metallurgy thanks to his reaching Michigan State University to study diary engineering. Now I learn from here (thanks to Arunn at Nanopolitan) that  a mix-up in the admissions office allowed Sir John Gurdon to study zoology instead of classics and he is the Nobel prize winner in Physiology and Medicine this year!

After receiving the report Sir John said he switched his attention to classics and was offered a place to study at Christ Church, Oxford, but was allowed to switch courses and read zoology instead because of a mix-up in the admissions office.

It was at Oxford as a postgraduate student that he published his groundbreaking research on genetics and proved for the first time that every cell in the body contains the same genes.

He did so by taking a cell from an adult frog’s intestine, removing its genes and implanting them into an egg cell, which grew into a clone of the adult frog.

The idea was controversial at the time because it contradicted previous studies by much more senior scientists, and it was a decade before the then-graduate student’s work became widely accepted.

But it later led directly to the cloning of Dolly the Sheep by Prof Ian Wilmut in 1996, and to the subsequent discovery by Prof Yamanaka that adult cells can be “reprogrammed” into stem cells for use in medicine.

The report in question is very interesting too. Take a look!

Technology, courage and fun

August 29, 2011

The basic personal start-up mechanism for research has to be curiosity. I find myself curious about how something works, or I observe something strange and begin to explore it. Because I am fond of symmetry, when I observe some simple symmetry, I am almost inexorably drawn into exploring it. For example, one day Don Oestreicher, who was then a graduate student, and I noticed that the number of random wires expected to cross the midsection of an N terminal printed circuit board is N/4 independent of whether the wires connect two or three terminals on the board. This comes about because although the probability of crossing is higher for wires connecting three terminals, 3/4 rather than 1/2, the number of wires is correspondingly reduced from N/2 to N/3. This simple observation led us to explore other wiring patterns, gather some data from real printed circuit boards, and eventually to publish a paper [4] called How Big Should a Printed Circuit Board Be? Follow your curiosity.

Beauty provides another form of personal encouragement for me. Some of the products of research are just pretty, although mathematicians prefer to use the word “elegant.” The simplicity of E=MC2, the elegance of information theory, and the power of an undecidability proof are examples. I got interested in asynchronous circuits by discovering a very simple form of first in first out (FIFO) storage that has rather complete symmetry [1,8]. It simply amazes me that my simple and symmetric circuit can “know” which way to pass data forward. The beauty itself piques my curiosity and flatters my pride.

Simplicity is to be valued in research results. Many students ask, “How long should my thesis be?” It would be better for them to ask, “How short can it be?” The best work is always simply expressed. If you find something simple to explore, do not turn it aside as trivial, especially if it appears to be new. In a very real sense, research is a form of play in which ideas are our toys and our objective is the creation of new castles from the old building block set. The courage to do research comes in part from our attraction to the simplicity and beauty of our creations.

I, for one, am and will always remain a practicing technologist. When denied my minimum daily adult dose of technology, I get grouchy. I believe that technology is fun, especially when computers are involved, a sort of grand game or puzzle with ever so neat parts to fit together. I have turned down several lucrative administrative jobs because they would deny me that fun. If the technology you do isn’t fun for you, you may wish to seek other employment. Without the fun, none of us would go on.

I tried to capture the spirit of research as a game in my paper about our walking robot [2]. Unfortunately, the editors removed from my paper all of the personal comments, the little poem about the robot by Claude Shannon, the pranks and jokes, and in short, the fun. The only fun they left was the title: Footprints in the Asphalt. All too often, technical reports are dull third person descriptions of something far away and impersonal. Technology is not far away and impersonal. It’s here, it’s intensely personal, and it’s great fun.

That is the last section of Ivan Sutherland’s Technology and Courage, a must-read piece. Link via Relevant History.

Make ’em Share ’em

March 4, 2011

That is the way to progress; the seed article talks about how bio-engineering benefits from tool making and sharing; it is true for scientific software too, by the way:

We do not know how to make biology easy to engineer (think playing with Legos or coding software with Java). However, technical inventions prototyped over the past six years point the way to a future in which biology is much easier to engineer relative to today. For example, in the summer of 2009, a team of undergraduates at the University of Cambridge won the International Genetically Engineered Machines (iGEM) competition by engineering seven strains of E. coli, each capable of synthesizing a different pigment visible to the naked eye. The resulting set, collectively known as E. chromi, required rerouting the metabolism of the bacteria so that natural precursor chemicals are converted across a palette of seven colors, from red to purple; such genetic color generators can be used to program microbes to change color in response to otherwise invisible environmental pollutants or health conditions. A few years ago such a project would have required several PhD-level experts in biology and metabolic engineering and would have likely taken a few years. Today, undergraduates can perform such work in months. This change in reality is due to two advances—tools and sharing—both of which are ready for their own revolutions.

Take a look!

Learning to do incorrect research

September 17, 2010

In the latest issue of EPW, there is a perspective piece by Donald W Attwood titled How I Learned to do Incorrect Research which might be worth your while (and, pray tell me, how do you NOT READ an essay titled thus?)

On a different note, Yes; I know. But, I am not able to figure out how to get the link for the pdf of the article at the EPW site.  Anyway, hurry before the piece disappears from the front page.

Research tips: work hard

July 22, 2009

Terry Tao; link via AMS graduate student blog which summarises the post nicely:

The two main problems are maintaining motivation and figuring out what to do next.  I have summarized some advice that Fields Medalist Terry Tao has written on the importance of working hard:1. Intelligence isn’t enough.

2. Reading and writing are important in addition to thinking.

3. Details are important in making your work worth reading and in selecting which papers are worth your time.

Take a look!

Solution by LOL

July 14, 2009

Quantum pontiff muses here (via Chad):

Actually I have a history for thinking that humor might be a decent indicator of good research or at least good problem solving (and not just a source of funny talks.) While a graduate student at Berkeley I participated in a San Francisco version of The Game (Fobik: there is a clue on these pages.) Basically this was a multihour (read: all night) puzzle hunt spread around the San Francisco bay area. The basic idea was that at each location there was a puzzle of some sort that you had to solve which would tell you the next location in the game. Imagine hundreds of geeks (including a world puzzle champion who was in my class at Caltech, #yeahrightlikeweweregoingtowin) piled into vans and cars racing from location to location, piling out of the car only to then sit around trying to solve a hard puzzle of some sort. Good stuff.

What does this have to do with humor and research? Well during this game I noticed something kind of interesting. Inevitably we would initially start working on the puzzle and someone would say something completely ridiculous. Like “I bet this puzzle is using flag semaphore!” Invariably, we would all laugh…yeah, right, like they would use semaphore in a puzzle involving chess. Then we would work for a while on the puzzle until someone had the audacity to think, “hey maybe it really does use semaphore.” And lo and behold, yeah that was the key to cracking the puzzle. This didn’t just happen once during “the game” but happened repeatedly (and not surprisingly as we got more tired, things got funnier, and we began to realize that the crazy funny idea we had right off the bat wasn’t something to laugh at, but was something to actually try!) Every time I’m trying to solve a problem these days, I often think, “what would be a funny solution?”

But I wonder if this solution method (“solution by LOL?” “SoLOLution?”) can’t be extended to a method for theory research.

Take a look!

PS: I am unable to resist this one: with humour as the indicator of good research, what is a classic? The paper that got most laughed at!

Single-molecule mechanics, gender, culture and mathematics performance, effect of tubulent driven instabilities on insect flight, and magnetic stabilization

June 3, 2009

Here are some interesting papers from the latest PNAS.

  1. Characterizing the resistance generated by a molecular bond as it is forcibly separated — L B Freund

    The goal of measurements of the resisting force generated by a molecular bond as it is being forcibly separated under controlled conditions is to determine functional characteristics of the bond. Here, we establish the dependence of force history during unbinding on both those parameters chosen to characterize the bond itself and the controllable loading parameters. This is pursued for the practical range of behavior in which unbinding occurs diffusively rather than ballistically, building on the classic work of Kramers. For a bond represented by a one-dimensional energy landscape, modified by a second time-dependent energy profile representing applied loading, we present a mathematical analysis showing the dependence of the resistance of the bond-on-bond well shape, general time dependence of the imposed loading, and stiffness of the loading apparatus. The quality of the result is established through comparison with full numerical solutions of the underlying Smoluchowski equation.

    A commentary on the paper is also available here.

  2. Gender, culture, and mathematics performance — J S Hyde and J E Mertz

    Using contemporary data from the U.S. and other nations, we address 3 questions: Do gender differences in mathematics performance exist in the general population? Do gender differences exist among the mathematically talented? Do females exist who possess profound mathematical talent? In regard to the first question, contemporary data indicate that girls in the U.S. have reached parity with boys in mathematics performance, a pattern that is found in some other nations as well. Focusing on the second question, studies find more males than females scoring above the 95th or 99th percentile, but this gender gap has significantly narrowed over time in the U.S. and is not found among some ethnic groups and in some nations. Furthermore, data from several studies indicate that greater male variability with respect to mathematics is not ubiquitous. Rather, its presence correlates with several measures of gender inequality. Thus, it is largely an artifact of changeable sociocultural factors, not immutable, innate biological differences between the sexes. Responding to the third question, we document the existence of females who possess profound mathematical talent. Finally, we review mounting evidence that both the magnitude of mean math gender differences and the frequency of identification of gifted and profoundly gifted females significantly correlate with sociocultural factors, including measures of gender equality across nations.

  3. Turbulence-driven instabilities limit insect flight performance — S A Combes and R Dudley

    Environmental turbulence is ubiquitous in natural habitats, but its effect on flying animals remains unknown because most flight studies are performed in still air or artificially smooth flow. Here we show that variability in external airflow limits maximum flight speed in wild orchid bees by causing severe instabilities. Bees flying in front of an outdoor, turbulent air jet become increasingly unstable about their roll axis as airspeed and flow variability increase. Bees extend their hindlegs ventrally at higher speeds, improving roll stability but also increasing body drag and associated power requirements by 30%. Despite the energetic cost, we observed this stability-enhancing behavior in 10 euglossine species from 3 different genera, spanning an order of magnitude in body size. A field experiment in which we altered the level of turbulence demonstrates that flight instability and maximum flight speed are directly related to flow variability. The effect of environmental turbulence on flight stability is thus an important and previously unrecognized determinant of flight performance.

  4. Magnetic stabilization and vorticity in submillimeter paramagnetic liquid tubes — J M D Coey et al

    It is possible to suppress convection and dispersion of a paramagnetic liquid by means of a magnetic field. A tube of paramagnetic liquid can be stabilized in water along a ferromagnetic track in a vertical magnetic field, but not in a horizontal field. Conversely, an “antitube” of water can be stabilized in a paramagnetic liquid along the same track in a transverse horizontal field, but not in a vertical field. The stability arises from the interaction of the induced moment in the solution with the magnetic field gradient in the vicinity of the track. The magnetic force causes the tube of paramagnetic liquid to behave as if it were encased by an elastic membrane whose cross-section is modified by gravitational forces and Maxwell stress. Convection from the tube to its surroundings is inhibited, but not diffusion. Liquid motion within the paramagnetic tube, however, exhibits vorticity in tubes of diameter 1 mm or less—conditions where classical pipe flow would be perfectly streamline, and mixing extremely slow. The liquid tube is found to slide along the track almost without friction. Paramagnetic liquid tubes and antitubes offer appealing new prospects for mass transport, microfluidics, and electrodeposition.

HowTo: be a genius

May 2, 2009

The latest research suggests a more prosaic, democratic, even puritanical view of the world. The key factor separating geniuses from the merely accomplished is not a divine spark. It’s not I.Q., a generally bad predictor of success, even in realms like chess. Instead, it’s deliberate practice. Top performers spend more hours (many more hours) rigorously practicing their craft.

Public discussion is smitten by genetics and what we’re “hard-wired” to do. And it’s true that genes place a leash on our capacities. But the brain is also phenomenally plastic. We construct ourselves through behavior. As Coyle observes, it’s not who you are, it’s what you do.

From this Op-Ed piece of David Brooks (which also got published in the Hindu today — though I see it in the hard copy, I am not able to locate the same online — Hindu is also going the way of some of the other online newspapers where the jungle of links is impenetrable!)

Simulations: how simple is too simple

March 25, 2009

One of the quotes that is attributed to Einstein is the following:

Make everything as simple as possible, but not simpler.

That brings us to the question, how simple is too simple?

Tomslee at Whimsley talks about this question, albeit only obliquely while discussing the need for (and uses of) simplified simulations:

The goal of simulations is not always to reproduce reality as closely as possible. In fact, building a finely-tuned, elaborate model of a particular phenomenon actually gets in the way of finding generalizations, commonalities, and trends, because with an accurate model you cannot find commonalities.

For example (and I’m not comparing my little blog post to any of these people’s work), in chemistry, Roald Hoffmann got a Nobel Prize and may be the most influential theorist of his generation because he chose to use a highly simplified model of electronic structure (the extended Huckel model). It is well known that the extended Huckel model fails to include the most elementary features needed to reproduce a chemical bond. Yet Hoffman was able to use this simple model to identify and explain huge numbers of trends among chemical structures precisely because it leaves out so many complicating factors. Later work using more sophisticated models like ab initio computations and density functional methods let you do much more accurate studies of individual molecules, but it’s a lot harder to extract a comprehensible model of the broad factors at work.

Or in economics, think of Paul Krugman’s description of an economy with two products (hot dogs and buns). Silly, but justifiably so. In fact, read that piece for a lovely explanation of why such a thought experiment is worthwhile.

Or elsewhere in social sciences, think of Thomas Schelling’s explorations of selection and sorting in Micromotives and Macrobehaviour, or of Robert Axelrod’s brilliantly overreaching The Evolution of Cooperation, which built a whole set of theories on a single two-choice game and influenced a generation of political scientists in the process. All these efforts work precisely because they look at simple and even unrealistic models. That’s the only way you can capture mechanisms: general causes that lead to particular outcomes. More precise models would not improve these works – they would just obscure the insights.

A good one!