by Artem Kaznatcheev in Evolutionary Games Group
Algorithmic information theory (AIT) allows us to study the inherent structure of objects, and qualify some as ‘random’ without reference to a generating distribution. The theory originated when Ray Solomonoff (1960), Andrey Kolmogorov (1965), and Gregory Chaitin (1966) looked at probability, statistics, and information through the algorithmic lens. Now the theory has become a central [...]... Read more »
Chaitin, G. (2009) Evolution of Mutating Software. EATCS Bulletin, 157-164. info:/
Je zit in je auto en draait wat aan de knop van de radio. Je hoort al snel of bepaalde muziek je bevalt of niet. Je herkent een stem, een liedje of zelfs de uitvoering ervan. Iedereen doet het, iedereen kan het. En vaak ook nog eens razendsnel: sneller dan een noot gemiddeld klinkt.Als u gevraagd zou worden om naar een reeks muziekfragmenten van 0,2 seconde te luisteren, zal blijken dat u met gemak aan kan geven welk fragment klassiek, jazz, R&B of pop is (zie luistertest). Een snippertje geluid geeft ons toegang tot de herinnering aan eerder gehoorde muziek, ook al hebben we deze serie noten nog nooit eerder gehoord. Die herinnering kan heel specifiek zijn: aan een liedje van Björk, bijvoorbeeld. Maar ze kan ook heel algemeen zijn: we herkennen een bepaald genre: klassiek, country, jazz. De nuances in klankkleur, karakteristiek voor een liedje of een heel genre, zitten kennelijk op een abstracte manier in ons geheugen opgeslagen. Daarom is de draaiknop (of tiptoets) van de autoradio zo’n succesvolle interface geworden…Vandaag verschenen er verschillende items in de media n.a.v. van een stukje in Volkskrant over de oorwurm en de hype rond Song Pop, een app die gebruik maakt van het hierboven beschreven muzikale talent dat we allemaal delen: het razendsnel herkennen van muziek.Over oorwurm: Volkskrant, NOS op 3 Over Song Pop App: Editie NL Gjerdingen, Robert O., & Perrott, D. (2008). Scanning the Dial: The Rapid Recognition of Music Genres Journal of New Music Research, 37 (2), 93-100 DOI: 10.1080/09298210802479268... Read more »
Gjerdingen, Robert O., & Perrott, D. (2008) Scanning the Dial: The Rapid Recognition of Music Genres. Journal of New Music Research, 37(2), 93-100. DOI: 10.1080/09298210802479268
Karlheinz Stockhausen is listening."Neue Musik ist anstrengend", wrote Die Zeit some time ago: "Der seit Pythagoras’ Zeiten unternommene Versuch, angenehme musikalische Klänge auf ganzzahlige Frequenzverhältnisse der Töne zurückzuführen, ist schon mathematisch zum Scheitern verurteilt. Außereuropäische Kulturen beweisen schließlich, dass unsere westliche Tonskala genauso wenig naturgegeben ist wie eine auf Dur und Moll beruhende Harmonik: Die indonesische Gamelan-Musik und Indiens Raga-Skalen klingen für europäische Ohren schräg."The definition of music as “sound” wrongly suggests that music, like all natural phenomena, adheres to the laws of nature. In this case, the laws would be the acoustical patterns of sound such as the (harmonic) relationships in the structure of the dominant tones, which determine the timbre. This is an idea that has preoccupied primarily the mathematically oriented music scientists, from Pythagoras to Hermann von Helmholtz. The first, and oldest, of these scientists, Pythagoras, observed, for example, that “beautiful” consonant intervals consist of simple frequency relationships (such as 2:3 or 3:4). Several centuries later, Galileo Galilei wrote that complex frequency relationships only “tormented” the eardrum. But, for all their wisdom, Pythagoras, Galilei, and like-minded thinkers got it wrong. In music, the “beautiful,” so-called “whole-number” frequency relationships rarely occur—in fact, only when a composer dictates them. The composer often even has to have special instruments built to achieve them, as American composer Harry Partch did in the twentieth century. Contemporary pianos are tuned in such a way that the sounds produced only approximate all those beautiful “natural” relationships. The tones of the instrument do not have simple whole number ratios, as in 2:3 or 3:4. Instead, they are tuned so that every octave is divided into twelve equal parts (a compromise to facilitate changes of key). The tones exist, therefore, not as whole number ratios of each other, but as multiples of 12√2 (1:1.05946).According to Galilei, each and every one of these frequency relationships are “a torment” to the ear. But modern listeners experience them very differently. They don’t particularly care how an instrument is tuned, otherwise many a concertgoer would walk out of a piano recital because the piano sounded out of tune. It seems that our ears adapt quickly to “dissonant” frequencies. One might even conclude that whether a piano is “in tune” or “out of tune” is entirely irrelevant to our appreciation of music. [fragment from Honing, 2011.]Julia Kursell (2011). Kräftespiel. Zur Dissymmetrie von Schall und Wahrnehmung. Zeitschrift für Medienwissenschaft, 2 (1), 24-40 DOI: 10.4472_zfmw.2010.0003Honing, H. (2012). Een vertelling. In S. van der Maas, C. Hulshof, & P. Oldenhave (Eds.), Liber Plurum Vocum voor Rokus de Groot (pp. 150-154). Amsterdam: Universiteit van Amsterdam (ISBN 978-90-818488-0-0).Whalley, Ian. (2006). William A. Sethares: Tuning, Timbre, Spectrum, Scale (Second Edition). Computer Music Journal, 30 (2) DOI: 10.1162/comj.2006.30.2.92... Read more »
Whalley, Ian. (2006) William A. Sethares: Tuning, Timbre, Spectrum, Scale (Second Edition). Computer Music Journal, 30(2). DOI: 10.1162/comj.2006.30.2.92
Last post we discussed robotically controlled biology. In this post we will talk about biologically controlled robots.The Hybrot: a rat neuron controlled robotIn 2001, S. Potter published a paper on the "Animat". A set of cultured neurons on a multi-electrode array (MEA, purple circle in above image) interfaced with a simulated robot. That is, not a physical moving around robot as pictured above, but a computer program simulating what a robot/animal could do. They made a virtual room for the animat to 'explore'. (If you can make a virtual environment for a worm, I suppose you can make one for a petri dish of cultured neurons) The signal from the cultured neurons determined where the animat went. If one group of neurons fired, the animat moved left, if another group fired it moved forward, and so forth. (The actual equations translating neuronal activity to animat movement were more complex than this, but you get the idea.) So here's the really cool thing: When the animat 'hit a wall', a set of neurons were stimulated with an electric pulse. They also gave the cultured neurons a sort of vestibular system, stimulating a different area depending on which direction the Animat was traveling.Although this Animat study was using a simulated environment and a simulated robot, using cultured neurons to control an actual robot was only a matter of time. Neurons are somehow even cooler when they are combined with robots, no?So what I think is really exciting about this reverse-cyborg system is that you can study the formation of neuronal networks in response to realistic experience. The feedback system used in the Animat could reveal how natural synaptic plasticity and other network-forming processes could organize a set of neurons. I am particularly interested in the effects of neuromodulation on these neurons. If they form a certain kind of network under normal conditions, how would that change if they were bathed in dopamine during the 'experience' or serotonin, or whatever. (Pick your favorite neurotransmitter).It is easy to think that this robot has a 'brain' but really the cultured neurons are not organized like the brain at all. Watching a network form in a dish is fascinating and can yield information about how neural networks form in general, but don't assume that this will tell us how networks actually form in an actual brain. Robots sure are cute (source)These methods can be used to discover really interesting things about neurons and networks, but other kinds of study (such as ones using real, intact brains) are need to find out what actually happens. © TheCellularScaleDemarse TB, Wagenaar DA, Blau AW, & Potter SM (2001). The Neurally Controlled Animat: Biological Brains Acting with Simulated Bodies. Autonomous robots, 11 (3), 305-310 PMID: 18584059... Read more »
Demarse TB, Wagenaar DA, Blau AW, & Potter SM. (2001) The Neurally Controlled Animat: Biological Brains Acting with Simulated Bodies. Autonomous robots, 11(3), 305-310. PMID: 18584059
What does the home pregnancy test and stained glass have in common? Both contain nanometer sized particles of metal (nanoparticles) that play a key role in how they work. The [...]... Read more »
Gao, B., Arya, G., & Tao, A.R. (2012) Self-orienting nanocubes for the assembly of plasmonic nanojunctions. Nature. DOI: 10.1038/NNANO.2012.83
A collaboration between a group in Imperial College and Media Interaction group in Japan yielded a really cool website: darwintunes.org. The idea is to apply Darwinian-like selection to music. Starting form a garble, after several generations producing something that is actually melodic and listen-able. Or a Katy Perry tune. Whatever. The selective force being the appeal of the tune to the listener. ... Read more »
This week an interesting study appeared in PNAS (early edition) showing that a simple Darwinian process can produce music. Inspired by cultural transmission theory, the study suggests that the evolution of music can be viewed and analyzed in terms of selection-variation processes, and, as such, may shed light on the evolution of real musical cultures. ... Read more »
I finally got a chance to see Prometheus this weekend and it reminded me why I love both technology and space so much. Without giving too much away for those of you that haven’t yet watched it, one of the more prominent ideas put forth in the movie is that we were created by alien [...]... Read more »
Ehrenfreund P, Spaans M, & Holm NG. (2011) The evolution of organic matter in space. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 369(1936), 538-54. PMID: 21220279
Ziurys LM. (2006) The chemistry in circumstellar envelopes of evolved stars: following the origin of the elements to the origin of life. Proceedings of the National Academy of Sciences of the United States of America, 103(33), 12274-9. PMID: 16894164
Kerr RA. (2012) Planetary science. Homegrown organic matter found on Mars, but no life. Science (New York, N.Y.), 336(6084), 970. PMID: 22628628
Davies PC. (2003) Does life's rapid appearance imply a Martian origin?. Astrobiology, 3(4), 673-9. PMID: 14987473
Cyborgs capture the imagination. Whether human-machine prosthetics or machine-insect spybots, the possibilities for medical advances and for exciting sci-fi novels are virtually endless. Remote controlled beetle from 1909 from Insect Lab A paper in 2009 by Sato et al. made some significant advances in the frontier of remote-controlled cyborg beetles. Specifically they were able to stimulate relatively specific neurons in these beetles to get them to initiate flight, and then were able to control the trajectory of the flying beetle by stimulating the muscles on either side of the beetle. Sato et al., 2009 Figure 1BThe remote-controlled beetle had to be relatively large to hold all the machinery. With technological advances to make the system smaller and lighter, it is likely that smaller insects could be used. So for all you paranoid people out there, don't worry, that tiny fly on your wall is not spying on you. It's too small for that. If you see a gigantic green beetle on your wall, now that's a different story. But just so you don't rest too easy: "As smaller and lower power microcontrollers and radios continue to appear on the market, researchers will be able to add an increasing amount of synthetic control into organic systems enabling new classes of programmable machines." Sato and Maharbiz, 2010As you might imagine, this paper comes packed full with supplemental videos of beetles flying. The following video is Video number 1 of the Sato et al. (2009) supplementary videos, all 13 of them are available (open access) at the Frontiers journal website. This video shows the initiation and cessation of flight in response to positive or negative electric pulses.And if you are more curious than freaked out by the possibility of remote-controlled bugs, you can make your own remote-controlled cockroach: The same geniuses who brought you the spikerbox, also provide the "RoboRoach". The kit that you can buy from backyard brains provides everything (except the cockroach) to make a remote-controlled cockroach. This doesn't implant into its brain, only into its sensory antennae. And it doesn't make the cockroach fly. It tricks the cockroach into thinking that it has touched something with its antennae, which makes it want to turn in the other direction. So even though it's not a super-spybot, it's as close as you can currently get to having your own cyborg pet. Next post I'll discuss the opposite approach to cyborg techonolgy: Controlling robots with biological signals. © TheCellularScaleSato H, & Maharbiz MM (2010). Recent developments in the remote radio control of insect flight. Frontiers in neuroscience, 4 PMID: 21629761Sato H, Berry CW, Peeri Y, Baghoomian E, Casey BE, Lavella G, Vandenbrooks JM, Harrison JF, & Maharbiz MM (2009). Remote radio control of insect flight. Frontiers in integrative neuroscience, 3 PMID: 20161808... Read more »
Sato H, & Maharbiz MM. (2010) Recent developments in the remote radio control of insect flight. Frontiers in neuroscience, 199. PMID: 21629761
Next weekend, a bunch of very distinguished computer scientists will rock up at the magnificent Manchester Town Hall for the Turing Centenary Conference in order to analyse the development of Computer Science, Artificial Intelligence and Alan Turing’s legacy .... Read more »
Last month, neuroscientists were warned about potential biases in SPM8, a popular software tool for analysis of fMRI data.Now a paper highlights another software pitfall: The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness MeasurementsFreeSurfer is one of the major image analysis packages and amongst other things, you can use it to measure the size of different parts of the brain. German researchers Ed Gronenschild and colleagues took a set of 30 brains and got FreeSurfer to estimate the size and thickness of various structures. Then they did the same thing, on the exact same brains, with a different version of the software.They found substantial differences in regional volumes, depending upon the version of FreeSurfer used. Running the same version of the software on a Mac vs a PC also created differences, and even the version of Mac OS had an impact.How much of a difference it made varied by brain location. The differences were 5-15% with version changes. For Mac vs PC and Mac OS updates it was less bad, 2-5% mostly, but in the worst regions - the parahippocampal and entorhinal cortex - it was still almost 15% different. Why those regions are so variable is unclear.The paper goes into lots more detail, but the lesson for researchers is extremely simple: don't cross the streams of data-analysis. Set up your analysis stream and then use it on all of your data. Same hardware, same software, same settings.Imagine you're doing a study comparing brain structure in two groups. Halfway through analyzing your data, you upgrade your MacOS. All of the brains you analyze after that will be, say, 5% "bigger". That'll certainly make your data much noisier, and if you happen to analyze most of Group A before Group B, it'll give you a false positive finding.Sometimes you just can't avoid changes in hardware or software - IT techs have a habit of upgrading things without asking - but in these cases, you should run the same data under the old and the new regime to see if it's making a difference.Finally, it would be wrong to blame FreeSurfer for this. I'd be surprised if they were any worse than the other software packages. Mixing and matching versions is something that the FreeSurfer developers specifically warn against. This paper shows why.Gronenschild EH, Habets P, Jacobs HI, Mengelers R, Rozendaal N, van Os J, and Marcelis M (2012). The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements. PloS one, 7 (6) PMID: 22675527... Read more »
Gronenschild EH, Habets P, Jacobs HI, Mengelers R, Rozendaal N, van Os J, & Marcelis M. (2012) The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements. PloS one, 7(6). PMID: 22675527
Enabling bioengineers to design new molecular machines for nanotechnology applications is one of the possible outcomes of a study by University of Montreal researchers that was published in Nature Structural and Molecular Biology yesterday (cited below). The scientists have developed a new approach to visualize how proteins assemble, which may also significantly aid our understanding [...]... Read more »
Vallée-Bélisle, A., & Michnick, S. (2012) Visualizing transient protein-folding intermediates by tryptophan-scanning mutagenesis. Nature Structural . DOI: 10.1038/nsmb.2322
TwitFight is a proof of concept application that uses several Natural Language Processing (NLP) techniques such as sentiment analysis or text mining to analyze two sets of "tweets" obtained by querying the Twitter API. ... Read more »
Bo Pang, Lillian Lee, & Shivakumar Vaithyanathan. (2002) Thumbs up? Sentiment Classification using Machine Learning Techniques. Proceedings of the ACL-02 conference on Empirical methods in natural language processing. arXiv: cs/0205070v1
EmotiMeter is a client-side application that continuously search for emoticons (happy / sad) in Twitter updates and draws a circle in a world map regarding the user location. ... Read more »
Bo Pang, Lillian Lee, & Shivakumar Vaithyanathan. (2002) Thumbs up? Sentiment Classification using Machine Learning Techniques. Proceedings of the ACL-02 conference on Empirical methods in natural language processing. arXiv: cs/0205070v1
There are already several great Android malware static and dynamic analysis frameworks (http://code.google.com/p/droidbox/, http://code.google.com/p/apkinspector/, http://code.google.com/p/androguard/ ) but I still wanted not only testing my first hypothesis about the higher correlation of non-standard Android permissions and malware but to be able to discover the most common permissions that malware authors use when developing these troublesome applications.... Read more »
B. Sanz, I. Santos, C. Laorden, X. Ugarte-Pedrero y P.G. Bringas. (2012) On the Automatic Categorisation of Android Applications. Proceedings of the 9th IEEE Consumer Communications and Networking Conference (CCNC). info:/
Everyone loves a good Hollywood ending. There’s nothing quite as satisfying as seeing a masked hero finally dispatch an evil villain. But aren’t flying men with super-strength a bit passé? Maybe it’s time for some new, cutting-edge superheroes…... Read more »
Velten A, Willwacher T, Gupta O, Veeraraghavan A, Bawendi MG, & Raskar R. (2012) Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging. Nature Communications, 745. PMID: 22434188
Wang Q, Tahir M, Zang J, & Zhao X. (2012) Dynamic electrostatic lithography: multiscale on-demand patterning on large-area curved surfaces. Advanced Materials, 24(15), 1947-51. PMID: 22419389
The scientific method begins with a hypothesis about our reality that can be tested via experimental observation. Hypothesis formation is iterative, building off prior scientific knowledge. Before one can form a hypothesis, one must have a thorough understanding of previous research to ensure that the path of inquiry is founded upon a stable base of established facts. But how can a researcher perform a thorough, unbiased literature review when over one million scientific articles are published annually? The rate of scientific discovery has outpaced our ability to integrate knowledge in an unbiased, principled fashion. One solution may be via automated information aggregation. In this manuscript we show that, by calculating associations between concepts in the peer-reviewed literature, we can algorithmically synthesize scientific information and use that knowledge to help formulate plausible low-level hypotheses.Oh man I've been waiting to write this post for over a year now. I'm so. Flippin'. Excited.I'm really proud to announce that our paper, "Automated Cognome Construction and Semi-automated Hypothesis Generation" has been accepted for publication in the Journal of Neuroscience Methods.Here's the pre-print PDF.I've been writing about this project on this blog for quite a while now, mostly in talking about brainSCANr and the many, many rejections we received while trying to publish it along the way.Seventeen journals to be exact. Which is fun to note in the Rejections & Failures section of my CV. It makes a game out of failing!I'll start by telling the story of how this project got started, then get into some of the more sciencey details.Back in May 2010 I was invited to speak at the (now) annual Cognitive Science Student Association (CSSA) Conference run by the undergraduate CogSci student association at Berkeley. They're an incredibly talented group and I've had a lot of fun working with them over the years.At that conference I sat on a Q&A panel with a hell of a group of scientists, including George Lakoff and the Chair of Stanford's Psychology department, James McClelland (who helped pioneer Parallel Distributed Processing).Berkeley CSSA ConferenceOn that panel I A'd many Qs, one of which was a fairly high-level question about the challenge of integrating the wealth of neuroscientific literature. It was a variant on the classic line that neuroscience is "data rich but theory poor". This is a problem I'd been struggling with for a long time and I'd had a few ideas.In my response I said that one of our problems as a field was that we had so many different people with different backgrounds speaking different jargons who aren't effectively communicating. I followed with an off-hand comment that "The Literature" was actually pretty smart when taken as a system, but that we individual puny brains just weren't bright enough to integrate all that information. I went on to claim that, if there was some way to automatically integrate information from the peer-review literature, we could probably glean a lot of new insights.Well James McClelland really seemed to disagree with me, but the idea kept kicking around my brain for a while.One night, several months later (while watching Battlestar Galactica with my wife), I turned to her and explained my idea. She asked me how I was planning on coding it up and, after I explained it, she challenged me by saying that she could definitely code that faster than I could.Fast-forward a couple of hours to around 2am and she had her results. Bah.The idea boils down to a very simple (and probably simplistic) assumption that the more frequently two neuroscientific terms appear in the title or abstracts of papers together, the more likely those terms are to be associated. For example, if "learning" and all of its synonyms appears in 100 papers with "memory" and all of its synonyms while both of those terms appear in a total of 1000 papers without one another, then the probability of those two terms being associated is 100/1000, or 0.1.We calculated such probabilities for every pair of terms using a dictionary that we manually curated. It contained 124 brain regions, 291 cognitive functions, and 47 diseases. Brain region names and associated synonyms were selected from the NeuroNames database, cognitive functions were obtained from Russ Poldrack's Cognitive Atlas, and disease names are from the NIH. The initial population of the dictionary was meant to represent the broadest, most plausibly common search terms that were also relatively unique (and thus likely not to lead to spurious connections).We counted the number of published papers containing pairs of terms using the National Library of Medicine's ESearch utility and the count return type. Here's the example for "prefrontal cortex" and "striatum":Conjunction:http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&field=word&term=("prefrontal+cortex"+OR+"prefrontal+cortices")+AND+("striatum"+OR+"neostriatum"+OR+"corpus+striatum")&rettype=countDisjunctions:http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&field=word&term=("prefrontal+cortex"+OR+"prefrontal+cortices")+NOT+("striatum"+OR+"neostriatum"+OR+"corpus+striatum")&rettype=counthttp://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&field=word&term=("striatum"+OR+"neostriatum"+OR+"corpus+striatum")+NOT+("prefrontal+cortex"+OR+"prefrontal+cortices")&rettype=countHere's what the method looks like:Voytek & Voytek - Figure 1We note in our manuscript that this method is rife with caveats, but this wasn't meant to be an end-point, but rather a proof-of-concept beginning.In the end we get a full matrix of 175528 term pairs. Once we got this database we hacking together the brainSCANr website to allow people to play around with terms and their relationships. We wanted to create a tool for researchers and the public alike to use to help simplify the complexities of neuroscience. You enter a search term, it shows the relationships and gives you links to the relevant peer-reviewed papers.As an example, here's Alzheimer's:brainSCANr Alzheimer's diseaseMy wife and co-author(!) Jessica Voytek and I threw the first version together (with help from my Uber ... Read more »
Voytek, J., & Voytek, B. (2012) Automated cognome construction and semi-automated hypothesis generation. Journal of Neuroscience Methods. DOI: 10.1016/j.jneumeth.2012.04.019
Schmidt M, & Lipson H. (2009) Distilling free-form natural laws from experimental data. Science (New York, N.Y.), 324(5923), 81-5. PMID: 19342586
Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, & Wager TD. (2011) Large-scale automated synthesis of human functional neuroimaging data. Nature methods, 8(8), 665-70. PMID: 21706013
Lein, E., Hawrylycz, M., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A., Boguski, M., Brockway, K., Byrnes, E.... (2006) Genome-wide atlas of gene expression in the adult mouse brain. Nature, 445(7124), 168-176. DOI: 10.1038/nature05453
Compared to a spindly mosquito, the mass of a raindrop is like a bus bearing down on a human. Yet the delicate insects thrive in wet, rainy climates. To find out how mosquitos live through rain showers, researchers pelted them with water drops while filming them at high speed. They saw that the insects' light weight, rather than being a liability, might be the key to their survival.
David Hu is a professor in both the biology and mechanical engineering departments at Georgia Tech. He's previously studied how water striders take advantage of fluid dynamics to skate across the surfaces of ponds. Andrew Dickerson, a graduate student in Hu's lab, has used high-speed video to find out how dogs and other animals shake water off of themselves. And in their newest study of animals getting wet, the team asks why a rain shower doesn't flatten every mosquito around.
The researchers trapped mosquitos in small mesh cages and sprayed them point-blank from above with jets of water. This Supersoaker-esque blast was similar to raindrops falling from the sky at terminal velocity. To get detailed video of collisions, they also hit mosquitos with drops falling at a slower speed.
The first thing they saw was that mosquitos made no effort to avoid the water. And they seemed to know what they were doing, because all the insects that got hit survived.
Going to the tape, the scientists saw that the consequence of getting hit by a raindrop depends on what part of the mosquito's body takes the blow. Since the insects are so lanky, 75% of hits happen on the legs or wings. This can throw a mosquito into a brief tumble or even a barrel roll, but it recovers without much trouble.
Direct hits to mosquitos' bodies are a different kind of carnival ride. The speeding raindrops glom onto the insects and propel them downward. Mosquitos captured on camera sometimes fell as far as 20 body lengths while being pushed by a raindrop. For a human, that would be a 12-story drop and a quick ending to the story. But mosquitos are able to pull away sideways from the raindrops and continue on their way, unharmed.
The only danger seems to come if mosquitos are flying close to the ground when they're hit, leaving themselves too little time to escape. The authors note that one unlucky bug was driven into a puddle and "ultimately perished."
To crunch some numbers—and find out why no mosquitos were being crunched—the researchers turned to substitute bugs that were simply Styrofoam balls of different sizes and weights. Although a raindrop isn't any bigger than a mosquito, the insect is extremely lightweight compared to the water. When the heavy drop hits the airy mosquito, it's almost like hitting nothing at all. And this, the researchers found, is what keeps the mosquitos alive. By offering barely any resistance, a mosquito minimize the force of the collision. The raindrop doesn't even splatter when it hits.
Of course, a bus hitting a human is pretty damaging no matter how little resistance the person put up. Mosquitos have the added advantage of a hard exoskeleton to help them resist the blow.
There's another reason this impact is survivable, David Hu explained in an email: Even though the force of the collision is 100 times the mosquito's mass, it's still only equal to the weight of a single feather. ("If we were in a comparable situation," he added, "we would not survive.")
If the impact didn't kill us, the acceleration would. Humans being hurled downward generally black out around 2 or 3 G's. But a mosquito suddenly driven toward the ground by a raindrop experiences an acceleration of 100 to 300 G's. The authors note that "insects struck by rain may achieve the highest survivable accelerations in the animal kingdom."
Although not especially useful to people trying to kill mosquitos or survive vertical bus collisions, the research could prove very handy to engineers designing insect-sized robotic aircraft. To fly successfully through rainstorms, these aircraft might adopt some of the mosquitos' technologies. A low mass would minimize the force of collisions. And sprawled legs, the authors write, could give tiny aircraft enough torque to pull away sideways from a falling drop. Mosquitos also have water-repellent hairs that may help them separate from stuck-on raindrops; aircraft could achieve the same thing with hydrophobic coatings.
Now if they would only design the miniature robot planes to attack the mosquitos, we'd have some real excitement.
Andrew K. Dickerson, Peter G. Shankles, Nihar M. Madhavan, & David L. Hu (2012). Mosquitoes survive raindrop collisions by virtue of their low mass PNAS : 10.1073/pnas.1205446109
Images courtesy of the laboratory of David L. Hu.
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Andrew K. Dickerson, Peter G. Shankles, Nihar M. Madhavan, & David L. Hu. (2012) Mosquitoes survive raindrop collisions by virtue of their low mass. PNAS. info:/10.1073/pnas.1205446109
My inactivity period was due to a lack of real news around the World. But I was not inactive at all. My friend Alfonso Farina presented to me another question that occupied my mind for the last weeks: What is the energy cost for computation? The first name that comes to mind in such a [...]... Read more »
Landauer, R. (1961) Irreversibility and Heat Generation in the Computing Process. IBM Journal of Research and Development, 5(3), 183-191. DOI: 10.1147/rd.53.0183
Bennett, C. (2003) Notes on Landauer's principle, reversible computation, and Maxwell's Demon. Studies In History and Philosophy of Science Part B: Studies In History and Philosophy of Modern Physics, 34(3), 501-510. DOI: 10.1016/S1355-2198(03)00039-X
Bérut, A., Arakelyan, A., Petrosyan, A., Ciliberto, S., Dillenschneider, R., & Lutz, E. (2012) Experimental verification of Landauer’s principle linking information and thermodynamics. Nature, 483(7388), 187-189. DOI: 10.1038/nature10872
A cellular automaton is a discrete model studied in computability theory and mathematics. It consists of an infinite, regular grid of cells, each in one of a finite number of states. The grid can be in any finite number of dimensions. Time is also discrete, and the state of a cell at time t is a function of the state of a finite number of cells called the neighborhood at time t-1. These neighbors are a selection of cells relative to some specified, and does not change (Though the cell itself may be in its neighborhood, it is not usually considered a neighbor). Every cell has the same rule for updating, based on the values in this neighbourhood. Each time the rules are applied to the whole grid a new generation is produced.The simplest nontrivial CA would be one-dimensional, with two possible states per cell, and a cell's neighbors defined as the cell on either side of it. A cell and its two neighbors forms a neighborhood of 3 cells, so there are 23=8 possible patterns for a neighborhood. So, there are 28=256 possible rules. These 256 CAs are generally referred to using a standard naming convention invented by Wolfram. The name of a CA is a number which, in binary, gives the rule table. Examples:Rule 90Rule 30http://amsqr.github.com/chromanin.js/ruletool.htmlReferences:Stephen Wolfram (2002). History of Cellular Automata A New Kind of Science... Read more »
Stephen Wolfram. (2002) History of Cellular Automata. A New Kind of Science. info:/
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