Tag Archives: open-access

Items of some interest:

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These are my recent Pin​board​.in links:

Items of some interest:

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These are my recent Pin​board​.in links:

  • [1006.5366] “Not only defended but also applied”: The per­ceived absur­dity of Bayesian inference

    “The mis­sion­ary zeal of many Bayesians of old has been matched, in the other direc­tion, by a view among some the­o­reti­cians that Bayesian meth­ods are absurd-​​not merely mis­guided but obvi­ously wrong in prin­ci­ple. We con­sider sev­eral exam­ples, begin­ning with Feller’s clas­sic text on prob­a­bil­ity the­ory and con­tin­u­ing with more recent cases such as the per­ceived Bayesian nature of the so-​​called dooms­day argu­ment. We ana­lyze in this note the intel­lec­tual back­ground behind var­i­ous mis­con­cep­tions about Bayesian sta­tis­tics, with­out aim­ing at a com­plete his­tor­i­cal cov­er­age of the rea­sons for this dismissal.”

    social-​​dynamics sta­tis­tics martial-​​arts-​​schools

  • [1206.3268] Fea­ture Selec­tion via Block-​​Regularized Regression

    “In this paper, we con­sid­ered the prob­lem of find­ing a sub­set of covari­ates in a high-​​dimensional space that affect the out­put vari­able when there is a block struc– ture in the covari­ates. In the con­text of asso­ci­a­tion map­ping, we pro­posed a regression-​​based model with a Markov chain prior that encodes the infor­ma­tion in the cor­re­la­tion struc­ture such as dis­tance and re– com­bi­na­tion rate between adja­cent SNP mark­ers. We demon­strated on the sim­u­lated and mouse data that our pro­posed algo­rithm can be used to iden­tify groups of SNP mark­ers as a rel­e­vant block of causal SNPs. The idea of rep­re­sent­ing the cor­re­la­tion struc­ture as a Markov chain in a vari­able selec­tion method to learn grouped rel­e­vant vari­ables can be gen­er­al­ized to use a graph­i­cal model as a prior in a vari­able selec­tion prob– lem to rep­re­sent an arbi­trary cor­re­la­tion struc­ture in vari­ables in a high-​​dimensional space. Another inter– est­ing exten­sion of the model is to model a struc­ture in out­put vari­ables as well when mea­sure­ments of mul– tiple out­put vari­ables are available.”

    sta­tis­tics bioin­for­mat­ics algo­rithms data-​​mining feature-​​extraction

  • Fil­ipe Kiss : A bet­ter git log

    “So, are you tired of this old and bored git log screen?”

    yes software-​​development git tricks-​​n-​​tips bash

  • Neu­roskep­tic: Brains are Dif­fer­ent on Macs

    “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.”

    data-​​analysis repro­ducibil­ity technical-​​assumptions anomalies-​​are-​​where-​​you-​​find-​​them

  • Plug: What is infer­en­tial­ism? « Odontomachus’s Blog

    “I’ve been crit­i­cal of objects and the idea of ref­er­ence for a while now. To me sen­tences and propo­si­tions, by virtue of their role as “moves” in social inter­ac­tions, are likely to have pri­or­ity in a prop­erly objec­tive account of mean­ing. Many puta­tive objects (e.g. cor­po­ra­tions or muta­ble dig­i­tal doc­u­ments) bor­der on being fic­tional, gain­ing their object­hood only through what we say about them; and many refer­ring phrases seem to refer to dif­fer­ent things, depend­ing on what is being pred­i­cated. I think this opin­ion would make me what Pere­grin calls a “strong infer­en­tial­ist”. Even­tu­ally I hope that think­ing clearly about seman­tics ought to (among other things) help bring calm to the cur­rent mass hys­te­ria which is the Seman­tic Web and Linked Data, and help steer all of that energy expen­di­ture to improve its consequence.”

    prag­ma­tism indirect-​​links phi­los­o­phy talking-​​about-​​thinking-​​and-​​the-​​reverse

  • [1206.3552] A Clas­si­fi­ca­tion for Com­mu­nity Dis­cov­ery Meth­ods in Com­plex Networks

    “In the last few years many real-​​world net­works have been found to show a so-​​called com­mu­nity struc­ture orga­ni­za­tion. Much effort has been devoted in the lit­er­a­ture to develop meth­ods and algo­rithms that can effi­ciently high­light this hid­den struc­ture of the net­work, tra­di­tion­ally by par­ti­tion­ing the graph. Since net­work rep­re­sen­ta­tion can be very com­plex and can con­tain dif­fer­ent vari­ants in the tra­di­tional graph model, each algo­rithm in the lit­er­a­ture focuses on some of these prop­er­ties and estab­lishes, explic­itly or implic­itly, its own def­i­n­i­tion of com­mu­nity. Accord­ing to this def­i­n­i­tion it then extracts the com­mu­ni­ties that are able to reflect only some of the fea­tures of real com­mu­ni­ties. The aim of this sur­vey is to pro­vide a man­ual for the com­mu­nity dis­cov­ery prob­lem. Given a meta def­i­n­i­tion of what a com­mu­nity in a social net­work is, our aim is to orga­nize the main cat­e­gories of com­mu­nity dis­cov­ery based on their own def­i­n­i­tion of com­mu­nity. Given a desired def­i­n­i­tion of com­mu­nity and the fea­tures of a prob­lem (size of net­work, direc­tion of edges, mul­ti­di­men­sion­al­ity, and so on) this review paper is designed to pro­vide a set of approaches that researchers could focus on.”

    via:cshalizi graph-​​theory com­mu­nity clas­si­fi­ca­tion algo­rithms nudge

  • [1205.0792] Exact Wavelets on the Ball

    “We develop an exact wavelet trans­form on the three-​​dimensional ball (i.e. on the solid sphere), which we name the fla­glet trans­form. For this pur­pose we first con­struct an exact har­monic trans­form on the radial line using damped Laguerre poly­no­mi­als and develop a cor­re­spond­ing quad­ra­ture rule. Com­bined with the spher­i­cal har­monic trans­form, this approach leads to a sam­pling the­o­rem on the ball and a novel three-​​dimensional decom­po­si­tion which we call the Fourier-​​Laguerre trans­form. We relate this new trans­form to the well-​​known Fourier-​​Bessel decom­po­si­tion and show that band-​​limitness in the Fourier-​​Laguerre basis is a suf­fi­cient con­di­tion to com­pute the Fourier-​​Bessel decom­po­si­tion exactly. We then con­struct the fla­glet trans­form on the ball through a har­monic tiling, which is exact thanks to the exact­ness of the Fourier-​​Laguerre trans­form (from which the name fla­glets is coined). The cor­re­spond­ing wavelet ker­nels have com­pact local­i­sa­tion prop­er­ties in real and har­monic space and their angu­lar aper­ture is invari­ant under radial trans­la­tion. We intro­duce a mul­tires­o­lu­tion algo­rithm to per­form the fla­glet trans­form rapidly, while cap­tur­ing all infor­ma­tion at each wavelet scale in the min­i­mal num­ber of sam­ples on the ball. Our imple­men­ta­tion of these new tools achieves float­ing point pre­ci­sion and is made pub­licly avail­able. We per­form numer­i­cal exper­i­ments demon­strat­ing the speed and accu­racy of these libraries and illus­trate their capa­bil­i­ties on a sim­ple denois­ing example.”

    wavelets geom­e­try representation-​​theory signal-​​processing answer-​​languages

  • [1205.3077] Efficiency-​​Revenue Trade-​​offs in Auctions

    “When agents with inde­pen­dent pri­ors bid for a sin­gle item, Myerson’s opti­mal auc­tion max­i­mizes expected rev­enue, whereas Vickrey’s second-​​price auc­tion opti­mizes social wel­fare. We address the nat­ural ques­tion of trade-​​offs between the two cri­te­ria, that is, auc­tions that opti­mize, say, rev­enue under the con­straint that the wel­fare is above a given level. If one allows for ran­dom­ized mech­a­nisms, it is easy to see that there are polynomial-​​time mech­a­nisms that achieve any point in the trade-​​off (the Pareto curve) between rev­enue and wel­fare. We inves­ti­gate whether one can achieve the same guar­an­tees using deter­min­is­tic mech­a­nisms. We pro­vide a neg­a­tive answer to this ques­tion by show­ing that this is a (weakly) NP-​​hard prob­lem. On the pos­i­tive side, we pro­vide polynomial-​​time deter­min­is­tic mech­a­nisms that approx­i­mate with arbi­trary pre­ci­sion any point of the trade-​​off between these two fun­da­men­tal objec­tives for the case of two bid­ders, even when the val­u­a­tions are cor­re­lated arbi­trar­ily. The major prob­lem left open by our work is whether there is such an algo­rithm for three or more bid­ders with inde­pen­dent val­u­a­tion distributions.”

    algo­rithms Pareto-​​front performance-​​measure multiobjective-​​optimization

  • Sym­bol­set

    “Sym­bol­sets are seman­tic sym­bol fonts. They work in mod­ern browsers and any­where Open­Type fea­tures are supported.”

    typog­ra­phy uni­code

  • [1204.6653] Elim­i­na­tion of Glass Arti­facts and Object Segmentation

    “Many images nowa­days are cap­tured from behind the glasses and may have cer­tain stains dis­crep­ancy because of glass and must be processed to make dif­fer­en­ti­a­tion between the glass and objects behind it. This research paper pro­poses an algo­rithm to remove the dam­aged or cor­rupted part of the image and make it con­sis­tent with other part of the image and to seg­ment objects behind the glass. The dam­aged part is removed using total vari­a­tion inpaint­ing method and seg­men­ta­tion is done using kmeans clus­ter­ing, anisotropic dif­fu­sion and water­shed trans­for­ma­tion. The final out­put is obtained by inter­po­la­tion. This algo­rithm can be use­ful to appli­ca­tions in which some part of the images are cor­rupted due to data trans­mis­sion or needs to seg­ment objects from an image for fur­ther processing.”

    image-​​segmentation image-​​processing nudge-​​targets algo­rithms

  • The whole of the law — Things from your life

    “But it’ll be your deci­sion, not iner­tia or fate. The ongo­ing cadence of ask­ing these ques­tions (and, maybe, the con­tent of any answers you come up with) will con­vene an open space for you to live in. A world where what­ever you do is right.”

    this

  • The Pirate Uni­ver­sity | Pirate university

    “The Pirate Uni­ver­sity is an on-​​line bul­letin board on which stu­dents post requests for aca­d­e­mic pub­li­ca­tions. You can com­pare it to an aca­d­e­mic wish list. Oth­ers, who know where to find these pub­li­ca­tions, reply and if pos­si­ble, pro­vide links to the resources searched. The Pirate Uni­ver­sity is not pro­vid­ing, stor­ing or shar­ing copy­righted mate­r­ial. An impor­tant ques­tion is if the upload­ing of arti­cles, pub­li­ca­tions is legal. If you are the copy­right holder of the arti­cle requested, there should be no prob­lem. Also in cer­tain cases, if you or your insti­tute have acquired the rights of the pub­li­ca­tion, or if it is free of rights, there shouldn’t be a prob­lem. It is prob­a­bly best to con­sult with your librar­ian to see which kind of pub­li­ca­tion is okay to share on the Internet.”

    academic-​​culture pub­lish­ing col­lab­o­ra­tion crowd­sourc­ing librar­i­ans open-​​access schol­ar­ship

  • [1206.3793] A dis­trib­uted classification/​estimation algo­rithm for sen­sor networks

    “…We pro­pose a novel coop­er­a­tive iter­a­tive algo­rithm which copes with the com­mu­ni­ca­tion con­straints imposed by the net­work and shows remark­able per­for­mance. Our main result is a rig­or­ous proof of the con­ver­gence of the algo­rithm and a char­ac­ter­i­za­tion of the limit behav­ior. We also show that, in the limit when the num­ber of sen­sors goes to infin­ity, the com­mon unknown para­me­ter is esti­mated with arbi­trary small error, while the clas­si­fi­ca­tion error con­verges to that of the opti­mal cen­tral­ized max­i­mum like­li­hood esti­ma­tor. We also show numer­i­cal results that val­i­date the the­o­ret­i­cal analy­sis and sup­port their pos­si­ble gen­er­al­iza­tion. We com­pare our strat­egy with the Expectation-​​Maximization algo­rithm and we dis­cuss trade-​​offs in terms of robust­ness, speed of con­ver­gence and imple­men­ta­tion simplicity.”

    distributed-​​processing collective-​​behavior sensor-​​networks algo­rithms nudge-​​targets

  • [1204.6391] Extend­ing par­tial rep­re­sen­ta­tions of func­tion graphs and per­mu­ta­tion graphs

    “Func­tion graphs are graphs rep­re­sentable by inter­sec­tions of con­tin­u­ous real-​​valued func­tions on the inter­val [0,1] and are known to be exactly the com­ple­ments of com­pa­ra­bil­ity graphs. As such they are rec­og­niz­able in poly­no­mial time. Func­tion graphs gen­er­al­ize per­mu­ta­tion graphs, which arise when all func­tions con­sid­ered are lin­ear. We focus on the prob­lem of extend­ing par­tial rep­re­sen­ta­tions, which gen­er­al­izes the recog­ni­tion prob­lem. We observe that for per­mu­ta­tion graphs an easy exten­sion of Golumbic’s com­pa­ra­bil­ity graph recog­ni­tion algo­rithm can be exploited. This approach fails for func­tion graphs. Nev­er­the­less, we present a polynomial-​​time algo­rithm for extend­ing a par­tial rep­re­sen­ta­tion of a graph by func­tions defined on the entire inter­val [0,1] pro­vided for some of the ver­tices. On the other hand, we show that if a par­tial rep­re­sen­ta­tion con­sists of func­tions defined on subin­ter­vals of [0,1], then the prob­lem of extend­ing this rep­re­sen­ta­tion to func­tions on the entire inter­val [0,1] becomes NP-​​complete.”

    graph-​​theory math-i-didn’t-know representation-​​theory ontol­ogy inter­est­ing

  • [1206.3294] Flex­i­ble Pri­ors for Exemplar-​​based Clustering

    “Exemplar-​​based clus­ter­ing meth­ods have been shown to pro­duce state-​​of-​​the-​​art results on a num­ber of syn­thetic and real-​​world clus­ter­ing prob­lems. They are appeal­ing because they offer com­pu­ta­tional ben­e­fits over latent-​​mean mod­els and can han­dle arbi­trary pair­wise sim­i­lar­ity mea­sures between data points. How­ever, when try­ing to recover under­ly­ing struc­ture in clus­ter­ing prob­lems, tai­lored sim­i­lar­ity mea­sures are often not enough; we also desire con­trol over the dis­tri­b­u­tion of clus­ter sizes. Pri­ors such as Dirich­let process pri­ors allow the num­ber of clus­ters to be unspec­i­fied while express­ing pri­ors over data par­ti­tions. To our knowl­edge, they have not been applied to exemplar-​​based mod­els. We show how to incor­po­rate pri­ors, includ­ing Dirich­let process pri­ors, into the recently intro­duced affin­ity prop­a­ga­tion algo­rithm. We develop an effi­cient max­prod­uct belief prop­a­ga­tion algo­rithm for our new model and demon­strate exper­i­men­tally how the expanded range of clus­ter­ing pri­ors allows us to bet­ter recover true clus­ter­ings in sit­u­a­tions where we have some infor­ma­tion about the gen­er­at­ing process.”

    clus­ter­ing algo­rithms

  • Mag­a­zine — The Case Against Cre­den­tial­ism — The Atlantic

    ’”ALL OF OUR WORK HAS GIVEN ME A VERY STRONG view,” Richard Boy­atzis told me one after­noon. The con­sult­ing firm Boy­atzis heads, McBer and Com­pany, was founded by David McClel­land in 1963. Its spe­cialty has been ana­lyz­ing what peo­ple actu­ally do in busi­ness jobs—not what their job descrip­tions say, but how they spend their time and which skills seem most impor­tant to their suc­cess. “I’ve come to see that when­ever a group insti­tutes a cre­den­tial­ing process, whether by licens­ing or insist­ing on advanced degrees, the espoused rhetoric is to enforce the stan­dards of pro­fes­sion­al­ism. This is true whether it’s among accoun­tants or plumbers or physi­cians. But the observed con­se­quences always seem to be these two: the exclu­sion of cer­tain groups, whether by inten­tion or not, and the estab­lish­ment of mediocre per­for­mance standards.“‘

    pro­fes­sion­al­iza­tion cre­den­tial­ing Andrew-​​Abbott-​​smiles-​​in-​​Chicago author­ity exper­tise cultural-​​assumptions disintermediation-​​targets

  • [1205.2483] Edge-​​clique graphs of cock­tail par­ties have unbounded rankwidth

    “In an attempt to find a polynomial-​​time algo­rithm for the edge-​​clique cover prob­lem on cographs we tried to prove that the edge-​​clique graphs of cographs have bounded rankwidth. How­ever, this is not the case. In this note we show that the edge-​​clique graphs of cock­tail party graphs have unbounded rank width.”

    open-​​questions nudge-​​targets graph-​​theory algo­rithms

  • [1206.3235] Iden­ti­fy­ing rea­son­ing pat­terns in games

    “We present an algo­rithm that iden­ti­fies the rea­son­ing pat­terns of agents in a game, by iter­a­tively exam­in­ing the graph struc­ture of its Multi-​​Agent Influ­ence Dia­gram (MAID) rep­re­sen­ta­tion. If the deci­sion of an agent par­tic­i­pates in no rea­son­ing pat­terns, then we can effec­tively ignore that deci­sion for the pur­pose of cal­cu­lat­ing a Nash equi­lib­rium for the game. In some cases, this can lead to expo­nen­tial time sav­ings in the process of equi­lib­rium cal­cu­la­tion. More­over, our algo­rithm can be used to enu­mer­ate the rea­son­ing pat­terns in a game, which can be use­ful for con­struct­ing more effec­tive com­put­er­ized agents inter­act­ing with humans.”

    game-​​theory infer­ence strat­egy nudge-​​targets learning-​​by-​​watching

Items of some interest:

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These are my recent Pin​board​.in links:

  • A List Apart: Arti­cles: Artis­tic Distance

    “While I’m sure that some­one will dis­agree, these sites have proven that very few “pro­fes­sion­als” have the abil­ity or courage to pro­vide a well-​​constructed analy­sis of some­one else’s work (whether or not the eval­u­a­tion was solicited). My opin­ion has noth­ing at all to do with either web­site, but rather with indus­try pro­fes­sion­als’ inabil­ity to chal­lenge, or fear of chal­leng­ing, the sta­tus quo. Far too often, hon­esty is met with ridicule, shame, or out­right rage from peo­ple hid­ing behind elec­tronic media. As a com­mu­nity, if our goal is to con­tinue rais­ing the bar for design, we need to get to a place where objec­tive dis­cus­sion is wel­comed, not scorned or drowned in obse­quious­ness. I would love to see dis­cus­sion of basic design move past the super­fi­cial trendi­ness of emerg­ing web technologies.”

    cri­tique col­lab­o­ra­tion advice graphic-​​design not-​​just

  • - How We Will Read: Laura Miller and Maud Newton

    “LM: Lit­er­ary peo­ple, when they talk about books, tend to think of fic­tion first. But most peo­ple, when they think about books, are think­ing about non­fic­tion, which lends itself amaz­ingly well to some kind of enhanced e-​​book expe­ri­ence. As a piece of that, I’m skep­ti­cal of enhanc­ing fic­tion e-​​books. The essence of nar­ra­tive is this sense of causal­ity and mean­ing, and when you intro­duce a lot of arbi­trary or ran­dom branch­ing things into it, it actu­ally loses it’s core plea­sure. It’s a tricky issue.”

    pub­lish­ing ebooks read­ing edi­tor

  • Per­sonal Tech for the 17th Cen­tury — Suzanne Fis­cher — Tech­nol­ogy — The Atlantic

    “The university’s John Carter Brown Library has long held the “Roger Williams Mys­tery Book,” a book that pur­port­edly belonged to Roger Williams, the rad­i­cal reli­gious thinker and founder of Rhode Island. The book is miss­ing its title page and thus has lit­tle iden­ti­fy­ing infor­ma­tion (besides a sub­ti­tle, “An Essay Con­cern­ing the Rec­on­cil­ing of Dif­fer­ences among Chris­tians”) — but it’s cov­ered with exten­sive short­hand mar­gin­a­lia sus­pected to have been writ­ten by Williams him­self some­time in the mid 1600s. The stu­dents, who include his­tory and math majors, are using this semes­ter to deci­pher the writ­ing and to deter­mine whether or not the short­hand hand­writ­ing was Williams’s hand.”

    nanohis­tory mar­gin­a­lia early-​​modern puz­zles

  • atomo

    “atomo is a small, sim­ple, insanely flex­i­ble and expres­sive pro­gram­ming lan­guage. its design is inspired by Scheme (small, sim­ple core), Slate (mul­ti­ple dis­patch, key­words), Ruby (very DSL-​​friendly), and Erlang (message-​​passing con­cur­rency). it is writ­ten in and pig­gy­backs on the Haskell run­time, per­mit­ting access to all of its power (and libraries!) through a thin layer.”

    pro­gram­ming lan­guage

  • Jour­nal of Dig­i­tal Humanities

    “The Jour­nal of Dig­i­tal Human­i­ties is a com­pre­hen­sive, peer-​​reviewed, open access jour­nal that fea­tures the best schol­ar­ship, tools, and con­ver­sa­tions pro­duced by the dig­i­tal human­i­ties com­mu­nity in the pre­vi­ous quarter.”

    digital-​​humanities jour­nal open-​​access pub­lish­ing

  • [1203.4881] Com­pu­ta­tional Com­plex­ity Analy­sis of Multi-​​Objective Genetic Programming

    Some days I just want to take genetic pro­gram­ming away from the com­puter sci­en­tists. Then I real­ize I ought to just let them keep the use­less, rit­u­al­ized thing they imag­ine it is.

    facepalm multiobjective-​​optimization software-​​development-​​is-​​not-​​programming

  • - How We Will Read: Clay Shirky

    “That is one of the poten­tial shifts in social read­ing: Can I cre­ate value for other peo­ple by say­ing that I found this pas­sage by Bruno LaTour strik­ing — even if I never look at it again? That’s an amaz­ing act of what I called “frozen shar­ing” in my last book. Being gen­er­ous about things when you are offer­ing it out to the pub­lic, with­out it being either in a spe­cific time frame or for a spe­cific target.”

    pub­lish­ing read­ing social-​​capital project be-​​useful-​​to-​​one-​​another

Items of some interest:

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These are my recent Pin​board​.in links:

Items of some interest:

Posted on by

These are my recent Pin​board​.in links:

  • Nicholas Rombes: Punk | berfrois

    “Most iron­i­cally, being based in the hope­lessly lost cul­tural void of Ann Arbor, a noto­ri­ous mecca for the last sur­viv­ing rem­nants of the pseudo-​​intellectual street peo­ple move­ment that said much and accom­plished little…”

    punk history-​​is-​​a-​​feature-​​not-​​a-​​bug cultural-​​dynamics ha-​​ha-​​only-​​semiserious

  • [1112.5309] POWERPLAY: Train­ing an Increas­ingly Gen­eral Prob­lem Solver by Con­tin­u­ally Search­ing for the Sim­plest Still Unsolv­able Problem

    An amus­ing col­lec­tion of what seem to be half-​​remembered ideas gleaned from his visit to the GPTP work­shop in Ann Arbor two years ago, pre­sented as his own inven­tions and with­out cita­tion or men­tion of the dozen peo­ple who actu­ally do this work. His keynote, as I remem­ber it, essen­tially revolved around him point­ing out how influ­en­tial his work should have been all along, if only we had both­ered to cite him as we should have done, because he thought up the core con­cepts of genetic pro­gram­ming well before any of us claimed we had. This is pretty much a camel’s-back straw for me. If there is a bet­ter argu­ment for com­pletely boy­cotting the cita­tion sys­tem and rely­ing on per­sonal asso­ci­a­tion and named schools rather than pub­li­ca­tion, I have not yet encoun­tered it. So remem­ber poor oppressed grad­u­ate and post­doc kids: when I cite your work by sim­ply nam­ing you per­son­ally, and not your advi­sor or your insti­tu­tion, and not even your pub­li­ca­tion or jour­nal but merely YOU PERSONALLY, it’s because you per­son­ally deserve the credit, not any of those other leeches. Got that?

    now-​​this-​​really-​​pisses-​​me-​​off-​​to-​​no-​​end

  • [1203.0856] Online Dis­crim­i­na­tive Dic­tio­nary Learn­ing for Image Clas­si­fi­ca­tion Based on Block-​​Coordinate Descent Method

    “Pre­vi­ous researches have demon­strated that the frame­work of dic­tio­nary learn­ing with sparse cod­ing, in which sig­nals are decom­posed as lin­ear com­bi­na­tions of a few atoms of a learned dic­tio­nary, is well adept to recon­struc­tion issues. This frame­work has also been used for dis­crim­i­na­tion tasks such as image clas­si­fi­ca­tion. To achieve bet­ter per­for­mances of clas­si­fi­ca­tion, experts develop sev­eral meth­ods to learn a dis­crim­i­na­tive dic­tio­nary in a super­vised man­ner. How­ever, another issue is that when the data become extremely large in scale, these meth­ods will be no longer effec­tive as they are all batch-​​oriented approaches. For this rea­son, we pro­pose a novel online algo­rithm for dis­crim­i­na­tive dic­tio­nary learn­ing, dubbed textbf{ODDL} in this paper. First, we intro­duce a lin­ear clas­si­fier into the con­ven­tional dic­tio­nary learn­ing for­mu­la­tion and derive a dis­crim­i­na­tive dic­tio­nary learn­ing prob­lem. Then, we exploit an online algo­rithm to solve the derived prob­lem. Unlike the most exist­ing approaches which update dic­tio­nary and clas­si­fier alter­nately via iter­a­tively solv­ing sub-​​problems, our approach directly explores them jointly. Mean­while, it can largely shorten the run­time for train­ing and is also par­tic­u­larly suit­able for large-​​scale clas­si­fi­ca­tion issues. To eval­u­ate the per­for­mance of the pro­posed ODDL approach in image recog­ni­tion, we con­duct some exper­i­ments on three well-​​known bench­marks, and the exper­i­men­tal results demon­strate ODDL is fairly promis­ing for image clas­si­fi­ca­tion tasks.”

    image-​​analysis image-​​segmentation algo­rithms nudge-​​targets

  • [1203.3271] The ther­mo­dy­nam­ics of prediction

    “A sys­tem respond­ing to a sto­chas­tic dri­ving sig­nal can be inter­preted as com­put­ing, by means of its dynam­ics, an (implicit) model of the envi­ron­men­tal vari­ables. The system’s state retains infor­ma­tion about past envi­ron­men­tal fluc­tu­a­tions, and a frac­tion of this infor­ma­tion is pre­dic­tive of future ones. The remain­ing non­pre­dic­tive infor­ma­tion reflects model com­plex­ity that does not improve pre­dic­tive power, and rep­re­sents the inef­fec­tive­ness of the model. We expose the fun­da­men­tal equiv­a­lence between this model inef­fi­ciency and ther­mo­dy­namic inef­fi­ciency, mea­sured by the energy dis­si­pated dur­ing the inter­ac­tion between sys­tem and envi­ron­ment. Our results hold arbi­trar­ily far from ther­mo­dy­namic equi­lib­rium and are applic­a­ble to a wide range of sys­tems, includ­ing bio­mol­e­c­u­lar machines. They high­light a pro­found con­nec­tion between the effec­tive use of infor­ma­tion and effi­cient ther­mo­dy­namic oper­a­tion: any sys­tem con­structed to keep mem­ory about its envi­ron­ment and to oper­ate ener­get­i­cally effi­ciently has to be predictive.”

    mod­el­ing philosophy-​​of-​​science information-​​theory physics ther­mo­dy­nam­ics talking-​​about-​​a-​​model-​​is-​​a-​​model pragmatism-it-ain’t

  • [1203.3434] On the Impact of Infor­ma­tion Tech­nolo­gies on Soci­ety: an His­tor­i­cal Per­spec­tive through the Game of Chess

    “The game of chess as always been viewed as an iconic rep­re­sen­ta­tion of intel­lec­tual prowess. Since the very begin­ning of com­puter sci­ence, the chal­lenge of being able to pro­gram a com­puter capa­ble of play­ing chess and beat­ing humans has been alive and used both as a mark to mea­sure hardware/​software pro­gresses and as an ongo­ing pro­gram­ming chal­lenge lead­ing to numer­ous dis­cov­er­ies. In the early days of com­puter sci­ence it was a topic for spe­cial­ists. But as com­put­ers were democ­ra­tized, and the strength of chess engines began to increase, chess play­ers started to appro­pri­ate to them­selves these new tools. We show how these inter­ac­tions between the world of chess and infor­ma­tion tech­nolo­gies have been her­ald of broader social impacts of infor­ma­tion tech­nolo­gies. The game of chess, and more broadly the world of chess (chess play­ers, lit­er­a­ture, com­puter soft­wares and web­sites ded­i­cated to chess, etc.), turns out to be a sur­pris­ingly and par­tic­u­larly sharp indi­ca­tor of the changes induced in our every­day life by the infor­ma­tion tech­nolo­gies. More­over, in the same way that chess is a mod­eliza­tion of war that cap­tures the raw fea­tures of strate­gic think­ing, chess world can be seen as small soci­ety mak­ing the study of the infor­ma­tion tech­nolo­gies impact eas­ier to ana­lyze and to grasp.”

    touch­stones his­tory algo­rithms history-​​of-​​science computer-​​science

  • Share Books | berfrois

    “Libraries are a recog­ni­tion that schol­ar­ship and cul­ture are more than the busi­ness of cre­at­ing and con­sum­ing. They are a human con­ver­sa­tion, and libraries pro­vide com­mon ground where that con­ver­sa­tion can take place and be remem­bered. By tak­ing aim at the right for the pub­lic to main­tain this con­ver­sa­tion and its mem­ory, pub­lish­ers have shown us what we have to lose. It’s time we resisted the out­sourc­ing of our com­mon her­itage by occu­py­ing the library.”

    Occupy libraries intellectual-​​property open-​​access public-​​policy activism

  • [1112.3307] Poly­tope Codes Against Adver­saries in Networks

    “Net­work cod­ing is stud­ied when an adver­sary con­trols a sub­set of nodes in the net­work of lim­ited quan­tity but unknown loca­tion. This prob­lem is shown to be more dif­fi­cult than when the adver­sary con­trols a given num­ber of edges in the net­work, in that lin­ear codes are insuf­fi­cient. To solve the node prob­lem, the class of Poly­tope Codes is intro­duced. Poly­tope Codes are con­stant com­po­si­tion codes oper­at­ing over bounded poly­topes in inte­ger vec­tor fields. The poly­tope struc­ture cre­ates addi­tional com­plex­ity, but it induces prop­er­ties on mar­ginal dis­tri­b­u­tions of code vec­tors so that validi­ties of code­words can be checked by inter­nal nodes of the net­work. It is shown that Poly­tope Codes achieve a cut-​​set bound for a class of pla­nar net­works. It is also shown that this cut-​​set bound is not always tight, and a tighter bound is given for an exam­ple network.”

    cryp­tog­ra­phy pri­vacy algo­rithms nudge-​​targets network-​​theory com­mu­ni­ca­tion

  • [1203.3353] Solv­ing Struc­ture with Sparse, Randomly-​​Oriented X-​​ray Data

    “Single-​​particle imag­ing exper­i­ments of bio­mol­e­cules at x-​​ray free-​​electron lasers (XFELs) require pro­cess­ing of hun­dreds of thou­sands (or more) of images that con­tain very few x-​​rays. Each low-​​flux image of the dif­frac­tion pat­tern is pro­duced by a sin­gle, ran­domly ori­ented par­ti­cle, such as a pro­tein. We demon­strate the fea­si­bil­ity of col­lect­ing data at these extremes, aver­ag­ing only 2.5 pho­tons per frame, where it seems doubt­ful there could be infor­ma­tion about the state of rota­tion, let alone the image con­trast. This is accom­plished with an expec­ta­tion max­i­miza­tion algo­rithm that processes the low-​​flux data in aggre­gate, and with­out any prior knowl­edge of the object or its ori­en­ta­tion. The ver­sa­til­ity of the method promises, more gen­er­ally, to rede­fine what mea­sure­ment sce­nar­ios can pro­vide use­ful sig­nal in the high-​​noise regime.”

    structural-​​biology image-​​analysis crys­tal­log­ra­phy algo­rithms inverse-​​problems nudge-​​targets sta­tis­tics

  • [1203.3203] An effi­cient algo­rithm for gen­er­at­ing AoA networks

    “The activ­i­ties, in project sched­ul­ing, can be rep­re­sented graph­i­cally in two dif­fer­ent ways, by either assign­ing the activ­i­ties to the nodes ‘AoN’ directed acyclic graph (dag) or to the arcs ‘AoA dag’. In this paper, a new algo­rithm is pro­posed for gen­er­at­ing, for a given project sched­ul­ing prob­lem, an Activity-​​on-​​Arc dag start­ing from the Activity-​​on-​​Node dag using the con­cepts of line graphs of graphs.”

    sched­ul­ing operations-​​research algo­rithms graph-​​theory

  • [1203.3341] A Com­par­i­son of Multi-​​Parametric Pro­gram­ming, Mixed-​​Integer Pro­gram­ming, Gra­di­ent Descent Based, and the Embed­ding Approach on Four Pub­lished Hybrid Opti­mal Con­trol Examples

    “…Com­mon mis­con­cep­tions regard­ing the embed­ding approach are addressed includ­ing whether or not it results in an aver­age value con­trol model (no), is nec­es­sary to “tweak” the algo­rithm to get bang-​​bang solu­tions (no), requires infi­nite switch­ing (no), has real-​​time capa­bil­ity (yes), or reduc­tion to a clas­si­cal non­lin­ear opti­miza­tion prob­lem (a desir­able yes).”

    control-​​theory operations-​​research algo­rithms numerical-​​methods philosophy-​​of-​​engineering design-​​patterns nudge-​​targets

  • [1203.3270] Extrac­tion of Facial Fea­ture Points Using Cumu­la­tive Histogram

    “This paper pro­poses a novel adap­tive algo­rithm to extract facial fea­ture points auto­mat­i­cally such as eye­brows cor­ners, eyes cor­ners, nos­trils, nose tip, and mouth cor­ners in frontal view faces, which is based on cumu­la­tive his­togram approach by vary­ing dif­fer­ent thresh­old val­ues. At first, the method adopts the Viola-​​Jones face detec­tor to detect the loca­tion of face and also crops the face region in an image. From the con­cept of the human face struc­ture, the six rel­e­vant regions such as right eye­brow, left eye­brow, right eye, left eye, nose, and mouth areas are cropped in a face image. Then the his­togram of each cropped rel­e­vant region is com­puted and its cumu­la­tive his­togram value is employed by vary­ing dif­fer­ent thresh­old val­ues to cre­ate a new fil­ter­ing image in an adap­tive way. The con­nected com­po­nent of inter­ested area for each rel­e­vant fil­ter­ing image is indi­cated our respec­tive fea­ture region. A sim­ple lin­ear search algo­rithm for eye­brows, eyes and mouth fil­ter­ing images and con­tour algo­rithm for nose fil­ter­ing image are applied to extract our desired cor­ner points auto­mat­i­cally. The method was tested on a large BioID frontal face data­base in dif­fer­ent illu­mi­na­tions, expres­sions and light­ing con­di­tions and the exper­i­men­tal results have achieved aver­age suc­cess rates of 95.27%.”

    image-​​segmentation image-​​analysis face-​​recognition algo­rithms nudge-​​targets

  • [1203.3284] Effi­cient Enu­mer­a­tion of the Directed Binary Per­fect Phy­lo­ge­nies from Incom­plete Data

    “We study a character-​​based phy­logeny recon­struc­tion prob­lem when an incom­plete set of data is given. More specif­i­cally, we con­sider the sit­u­a­tion under the directed per­fect phy­logeny assump­tion with binary char­ac­ters in which for some species the states of some char­ac­ters are miss­ing. Our main object is to give an effi­cient algo­rithm to enu­mer­ate (or list) all per­fect phy­lo­ge­nies that can be obtained when the miss­ing entries are com­pleted. While a sim­ple branch-​​and-​​bound algo­rithm (B&B) shows a the­o­ret­i­cally good per­for­mance, we pro­pose another approach based on a zero-​​suppressed binary deci­sion dia­gram (ZDD). Exper­i­men­tal results on ran­domly gen­er­ated data exhibit that the ZDD approach out­per­forms B&B. We also prove that count­ing the num­ber of phy­lo­ge­netic trees con­sis­tent with a given data is #P-​​complete, thus pro­vid­ing an evi­dence that an effi­cient ran­dom sam­pling seems hard.”

    phy­lo­ge­net­ics inverse-​​problems genet­ics algo­rithms sta­tis­tics nudge-​​targets

  • [1203.0879] Design­ing and using prior knowl­edge for phase retrieval

    “In this work we develop an algo­rithm for sig­nal recon­struc­tion from the mag­ni­tude of its Fourier trans­form in a sit­u­a­tion where some (non-​​zero) parts of the sought sig­nal are known. Although our method does not assume that the known part com­prises the bound­ary of the sought sig­nal, this is often the case in microscopy: a spec­i­men is placed inside a known mask, which can be thought of as a known light source that sur­rounds the unknown sig­nal. There­fore, in the past, sev­eral algo­rithms were sug­gested that solve the phase retrieval prob­lem assum­ing known bound­ary val­ues. Unlike our method, these meth­ods do rely on the fact that the known part is on the bound­ary. Besides the recon­struc­tion method we give an expla­na­tion of the phe­nom­ena observed in pre­vi­ous work: the recon­struc­tion is much faster when there is more energy con­cen­trated in the known part. Quite sur­pris­ingly, this can be explained using our pre­vi­ous results on phase retrieval with approx­i­mately known Fourier phase.”

    image-​​analysis image-​​processing learn­ing inverse-​​problems algo­rithms nudge-​​targets

  • [1203.3415] A New Approach to Count Pat­tern Motifs Using Com­bi­na­to­r­ial Techniches

    “We pro­posed two new exact algo­rithms to detect net­work motifs of size 3 and 4. Con­sid­er­ing that motifs need to count the iso­mor­phic pat­terns in the orig­i­nal graph $G(V,E)$ and in a set of ran­dom­ized graphs, the fol­low­ing com­plex­i­ties con­cern about count iso­mor­phic pat­terns in a sin­gle graph. Let $m=|E|$ and let $a(G)$ be the arboric­ity of $G$. Assume $|E|geq|V|$. We describe a $O(a(G)m)$ time com­plex­ity algo­rithm to count iso­mor­phic pat­terns of size 3. The com­plex­ity is a $O({msqrt{m}})$ in the worst graph. The sec­ond algo­rithm is a $O(m^2)$ com­plex­ity algo­rithm to count iso­mor­phic pat­terns of size 4. The final result was expres­sive faster when com­pared with other imple­mented algorithms.”

    network-​​theory graph-​​theory algo­rithms nudge-​​targets