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  • July 8, 2012
  • 09:24 PM

Visualizing fields of research based on readership

by Peter Kraker in Science and the Web (Peter Kraker's weblog)

Social reference management systems provide a wealth of information that can be used for the analysis of science. In this paper, we examine whether user library statistics can produce meaningful results with regards to science evaluation and knowledge domain visualization. We are conducting two empirical studies, using a sample of library data from Mendeley, the worlds largest social reference management system. Based on the occurrence of references in users libraries, we perform a large-scale impact factor analysis and an exploratory co-readership analysis. Our preliminary find- ings indicate that the analysis of user library statistics can produce accurate, timely, and content-rich results. We find that there is a significant relationship between the impact factor and the occurrence of references in libraries. Using a knowledge domain visualization based on co-occurrence measures, we are able to identify two areas of topics within the emerging field of technology-enhanced learning.... Read more »

Peter Kraker, Christian Körner, Kris Jack, & Michael Granitzer. (2012) Harnessing User Library Statistics for Research Evaluation and Knowledge Domain Visualization. Proceedings of the 21st International Conference Companion on World Wide Web , 1017-1024. DOI: 10.1145/2187980.2188236  

  • July 8, 2012
  • 04:24 PM

Visualizing fields of research based on readership

by Peter Kraker in Science and the Web

I haven’t blogged lately, mostly due to the fact that I was busy moving to London. I will be with Mendeley for the next four months in the context of the Marie Curie project TEAM. My first week is over now, and I have already started to settle in thanks to the great folks at …Read More... Read more »

Peter Kraker, Christian Körner, Kris Jack, & Michael Granitzer. (2012) Harnessing User Library Statistics for Research Evaluation and Knowledge Domain Visualization. Proceedings of the 21st International Conference Companion on World Wide Web , 1017-1024. DOI: 10.1145/2187980.2188236  

  • July 6, 2012
  • 10:14 AM

Advances in Neuronal Destruction

by TheCellularScale in The Cellular Scale

Destroying neurons is not difficult.  Destroying specific neurons, but leaving others intact is another story. Ablating specific neurons usually involves fancy genetic trickery, but it can also be accomplished with fancy mechanical lasers! Laser near cell (source)A new study published in PNAS (Hayes et al., 2012) uses the cells own rhythm generating properties to target the neurons for destruction.Specifically, Hayes et al. is investigating the breathing neurons. These neurons are in the Pre-Botzinger Complex (preBotC) of the Medulla and they control the inhalation phase of breathing.  They work together as a complex to generate rhythms even in a brain slice.  Using a calcium-sensitive dye, Hayes et al. could tell which neurons were participating in the rhythm generation. The breathing neurons show specific calcium patterns, increasing and decreasing with a frequency of 0.15-0.5Hz.  The breathing neurons are located and the specific spatial coordinates of each neuron is saved.  A mechanically controlled laser can then automatically target each specific neuron for destruction (red dots in figure below).  Hayes et al., 2012 Figure 1Because silencing the neurons (NK1R-containing) in the preBotC completely stops breathing, they wanted to see how many neurons could be destroyed before the rhythm stopped.  And they wanted to see how it stopped.  Is there some magic number of cells that are needed to maintain the rhythmic output? or does the rhythm slowly decrease in amplitude?  So measuring the XII nerve for output, they began randomly destroying the rhythmic cells one by one. They found that destroying these neurons one by one caused a decrease in amplitude and frequency of the XII nerve output and eventually stopped it entirely.  It took about 120 neurons to completely stop the rhythm, but the weird thing is that even after destroying 120 neurons, the rhythm continued for about half an hour.  The mechanisms underlying this delay are not completely clear, but the authors attribute it to the slow effects of a decrease in mGluR stimulation.  This new technique is pretty exciting because it allows the sequential deletion of specific cells.  Even the study erasing memories cell by cell didn't actually delete the cells one at a time. This technique is especially interesting for investigating the way that a collection of individual cells create emergent network properties.  Now questions like 'how many cells are needed to form or maintain a functional network?' and 'which cells are necessary for the network's function?' can be answered.  © TheCellularScaleHayes JA, Wang X, & Del Negro CA (2012). Cumulative lesioning of respiratory interneurons disrupts and precludes motor rhythms in vitro. Proceedings of the National Academy of Sciences of the United States of America, 109 (21), 8286-91 PMID: 22566628... Read more »

Hayes JA, Wang X, & Del Negro CA. (2012) Cumulative lesioning of respiratory interneurons disrupts and precludes motor rhythms in vitro. Proceedings of the National Academy of Sciences of the United States of America, 109(21), 8286-91. PMID: 22566628  

  • July 3, 2012
  • 09:13 AM

Artificial Cerebellum in Robotics Developed

by Jason Carr in Wired Cosmos

University of Granada researchers have developed an artificial cerebellum (a biologically-inspired adaptive microcircuit) that controls a robotic arm with human-like precision. The cerebellum is the part of the human brain that controls the locomotor system and coordinates body movements.... Read more »

  • July 2, 2012
  • 04:35 AM

How 16,000 Processors Learned to Actually ‘See’

by United Academics in United Academics

In their search for knowledge on complex data processing, the group has created a huge surrogate neural-like network of 16,000 connected computer processors, which share about one billion connections.... Read more »

Quoc, V. Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean, Andrew Y. Ng. (2012) Building High-level Features Using Large Scale Unsupervised Learning. info:/

  • June 29, 2012
  • 04:42 PM

How fireworks light up the sky

by Cath in Basal Science (BS) Clarified

Many countries/regions will be celebrating their national/independence day over the weekend and into next week, so you’ll likely have a chance to see some fireworks whether in person, on television, [...]... Read more »

  • June 29, 2012
  • 01:00 PM

Is Chaitin proving Darwin with metabiology?

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:/

  • June 27, 2012
  • 07:20 AM

Herkent u deze melodie? [Dutch]

by Henkjan Honing in Music Matters

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  

  • June 25, 2012
  • 09:11 AM

Why does a well-tuned modern piano not sound out-of tune?

by Henkjan Honing in Music Matters

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 »

Julia Kursell. (2011) Kräftespiel. Zeitschrift für Medienwissenschaft, 2(1), 24-40. DOI: 10.4472_zfmw.2010.0003  

  • June 21, 2012
  • 03:21 PM

Neuron-controlled robots: reverse-cyborgs

by TheCellularScale in The Cellular Scale

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 »

  • June 20, 2012
  • 11:10 PM

A bright future with self-assembling nanocubes

by Cath in Basal Science (BS) Clarified

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 »

  • June 19, 2012
  • 11:29 AM

The Evolution of Music

by Iddo Friedberg in Byte Size Biology

A collaboration between a group in Imperial College and Media Interaction group in Japan yielded a really cool website: 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 »

Robert M. MacCallum, Matthias Mauchb, Austin Burta, & Armand M. Leroia. (2012) (2012-06-18) Evolution of music by public choice. . Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.1203182109  

  • June 19, 2012
  • 10:25 AM

Can artificial music evolve in a Darwinian way?

by Henkjan Honing in Music Matters

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 »

Robert M. MacCalluma, Matthias Mauch, Austin Burta, & Armand M. Leroia. (2012) Evolution of music by public choice. Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.1203182109  

  • June 19, 2012
  • 01:23 AM

Do I Look Like an Alien to You?

by Jason Carr in Wired Cosmos

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  

  • June 17, 2012
  • 12:45 PM

Remote-controlled cyborg insects

by TheCellularScale in The Cellular Scale

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, 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, 24. PMID: 20161808  

  • June 15, 2012
  • 10:49 AM

Alan Turing Centenary Conference, 22nd-25th June 2012

by Duncan Hull in O'Really?

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 [1].... Read more »

  • June 14, 2012
  • 03:27 PM

Brains are Different on Macs

by Neuroskeptic in Neuroskeptic

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 »

  • June 11, 2012
  • 07:37 AM

Molecular Machines for Nanotech Applications

by Jason Carr in Wired Cosmos

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 »

  • June 9, 2012
  • 11:00 AM

Twit-Fight: A Sentiment Analysis Demo using Twitter Data

by Alejandro Mosquera in amsqr

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

  • June 9, 2012
  • 11:00 AM

Android malware classification using NLTK

by Alejandro Mosquera in amsqr

There are already several great Android malware static and dynamic analysis frameworks (,, ) 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:/

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