Tag Archives: nudge

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:

Posted on by

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

  • [1206.3340] Extrac­tion of Deep Phy­lo­ge­netic Sig­nal and Improved Res­o­lu­tion of Evo­lu­tion­ary Events within the recA/​RAD51 Phylogeny

    “The recA/​RAD51 gene fam­ily encodes a diverse set of recom­bi­nase pro­teins that effect homol­o­gous recom­bi­na­tion, DNA-​​repair, and genome sta­bil­ity. The recA gene fam­ily is expressed in almost all species of Eubac­te­ria, Archaea, and Eukary­otes, and even in some viruses. To date, efforts to resolve the deep evo­lu­tion­ary ori­gins of this ancient pro­tein fam­ily have been hin­dered, in part, by the high sequence diver­gence between fam­i­lies (i.e. ~30% iden­tity between par­al­o­gous groups). Through (i) large taxon sam­pling, (ii) the use of a phy­lo­ge­netic algo­rithm designed for mea­sur­ing highly diver­gent par­alogs, and (iii) novel Evo­lu­tion­ary Spa­tial Dynam­ics sim­u­la­tion and ana­lyt­i­cal tools, we obtained a robust, par­si­mo­nious and more refined phy­lo­ge­netic his­tory of the recA/​RAD51 super­fam­ily. Taken together, our model for the evo­lu­tion of recA/​RAD51 fam­ily pro­vides a bet­ter under­stand­ing of ancient ori­gin of recA pro­teins and mul­ti­ple events lead­ing to the diver­si­fi­ca­tion of recA homologs in eukary­otes, includ­ing the dis­cov­ery of addi­tional RAD51 sub-​​families.”

    cladis­tics algo­rithms visu­al­iza­tion deep-​​time sta­tis­tics

  • [1204.6547] Gen­er­at­ing self-​​organizing col­lec­tive behav­ior using sep­a­ra­tion dynam­ics from exper­i­men­tal data

    “Math­e­mat­i­cal mod­els for sys­tems of inter­act­ing agents using sim­ple local rules have been pro­posed and shown to exhibit emer­gent swarm­ing behav­ior. Most of these mod­els are con­structed by intu­ition or man­ual obser­va­tions of real phe­nom­ena, and later tuned or ver­i­fied to sim­u­late desired dynam­ics. In con­trast to this approach, we pro­pose using a model that attempts to fol­low an aver­aged rule of the essen­tial distance-​​dependent col­lec­tive behav­ior of real pigeon flocks, which was abstracted from exper­i­men­tal data. By using a sim­ple model to fol­low the behav­ioral ten­den­cies of real data, we show that our model can exhibit emer­gent self-​​organizing dynam­ics such as flock­ing, pat­tern for­ma­tion, and counter-​​rotating vor­tices. The range of behav­iors observed in our sim­u­la­tions are richer than the stan­dard mod­els of col­lec­tive dynam­ics, and should thereby give poten­tial for new mod­els of com­plex behavior.”

    agent-​​based swarms boids algo­rithms emergent-​​design

  • [1206.3555] A Dynamic Pro­gram­ming Algo­rithm for Infer­ence in Recur­sive Prob­a­bilis­tic Programs

    “We describe a dynamic pro­gram­ming algo­rithm for com­put­ing the mar­ginal dis­tri­b­u­tion of dis­crete prob­a­bilis­tic pro­grams. This algo­rithm takes a func­tional inter­preter for an arbi­trary prob­a­bilis­tic pro­gram­ming lan­guage and turns it into an effi­cient mar­gin­al­izer. Because direct caching of sub-​​distributions is impos­si­ble in the pres­ence of recur­sion, we build a graph of depen­den­cies between sub-​​distributions. This fac­tored sum-​​product net­work makes (poten­tially cyclic) depen­den­cies between sub­prob­lems explicit, and cor­re­sponds to a sys­tem of equa­tions for the mar­ginal dis­tri­b­u­tion. We solve these equa­tions by fixed-​​point iter­a­tion in topo­log­i­cal order. We illus­trate this algo­rithm on exam­ples used in teach­ing prob­a­bilis­tic mod­els, com­pu­ta­tional cog­ni­tive sci­ence research, and game theory.”

    recur­sion stochastic-​​programming sim­u­la­tion nudge

  • Share­able: Hack­ing Home: Col­iv­ing Rein­vents the Com­mune for a Net­worked Age

    ‘It was more than just a lux­ury home full of bril­liant young minds. Dubbed “an inten­tional com­mu­nity”, The Rain­bow Man­sion was an exper­i­ment in a new type of cohab­i­ta­tion. The house began host­ing hackathons and salons in its library, invit­ing Sil­i­con Valley’s best and bright­est to par­tic­i­pate. “Right away it set itself in motion,” Schin­gler says. “It had this sort of acci­den­tal mys­tique about it.”’

    cohous­ing col­lab­o­ra­tion nerd-​​culture

  • [1206.3369] A Suc­ces­sive Approx­i­ma­tion Algo­rithm for Com­put­ing the Divi­sor Sum­ma­tory Function

    “An algo­rithm is pre­sented to com­pute iso­lated val­ues of the divi­sor sum­ma­tory func­tion in O(n^(1/3)) time and O (log n) space. The algo­rithm is ele­men­tary and uses a geo­met­ric approach of suc­ces­sive approx­i­ma­tion com­bined with coor­di­nate transformation.”

    algo­rithms computational-​​geometry nudge-​​targets

  • [1204.3650] Evo­lu­tion­ary Meta­dy­nam­ics: a Novel Method to Pre­dict Crys­tal Structures

    “A novel method for crys­tal struc­ture pre­dic­tion, based on meta­dy­nam­ics and evo­lu­tion­ary algo­rithms, is pre­sented here. This tech­nique can be used to pro­duce effi­ciently both the ground state and metastable states eas­ily reach­able from a rea­son­able ini­tial struc­ture. We use the cell shape as col­lec­tive vari­able and evo­lu­tion­ary vari­a­tion oper­a­tors devel­oped in the con­text of the USPEX method [Oganov, Glass, textit{J. Chem. Phys.}, 2006, textbf{124}, 244704; Lyakhov textit{et al., Comp. Phys. Comm.}, 2010, textbf{181}, 1623; Oganov textit{et al., Acc. Chem. Res.}, 2011, textbf{44}, 227] to equi­li­brate the sys­tem as a func­tion of the col­lec­tive vari­ables. We illus­trate how this approach helps one to find sta­ble and metastable states for Al$_2$SiO$_5$, SiO$_2$, MgSiO$_3$, and car­bon. Apart from pre­dict­ing crys­tal struc­tures, the new method can also pro­vide insight into mech­a­nisms of phase transitions.”

    evolutionary-​​algorithms search-​​algorithms physics nudge-​​targets condensed-​​matter

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  • What if Inter­ac­tiv­ity is the New Pas­siv­ity? Jonathan Sterne /​ McGill Uni­ver­sity | Flow

    “What if all the bad things that media crit­ics have been said about pas­siv­ity for the past cen­tury or two are now equally applic­a­ble to all the demands to inter­act, to par­tic­i­pate? What if inter­ac­tiv­ity is now one of the cen­tral hinges through which power works? In many moments today, the most com­pli­ant ges­ture we can make is to con­sent to inter­act on the terms pre­sented to us by our soft­ware and machines. This pull is espe­cially strong in those com­mer­cial plat­forms that cel­e­brate their own dif­fer­ence from the so-​​called pas­sive media of pre­vi­ous decades, and in the process mon­e­tize their users’ par­tic­i­pa­tion either directly or indi­rectly. What if—from time to time—we chose not to iden­tify with the inter­ac­tive promise of new media plat­forms or for that mat­ter new media art? What if, when the new media savants lam­bast so-​​called old media audi­ences as denizens of pas­siv­ity and ide­ol­ogy, we say, “yes, that’s me”?”

    a-​​bit-​​too-​​theoryish cultural-​​norms ingroup-​​outgroup new-​​media

  • How Can Her­bert Spencer’s 1892 Revi­sions to his Social Sta­t­ics Help Us Under­stand Con­ser­v­a­tive Oppo­si­tion to the Indi­vid­ual Man­date? | Rortybomb

    “But I think it’s clear what his real objec­tion was: uni­ver­sal suf­frage has the poten­tial to advance social­is­tic causes, inter­fer­ing with his laissez-​​faire project. From his auto­bi­og­ra­phy: “Another exten­sion of the fran­chise since made…will inevitably be fol­lowed by a still more rapid growth of social­is­tic leg­is­la­tion.” When he real­ized women’s equal­ity could poten­tially inter­fere with laissez-​​faire eco­nom­ics, it was time for women’s equal­ity to get cut from his over­all the­ory of a bet­ter world. He would rather muti­late his intel­lec­tual project instead of allow­ing his ene­mies to con­tinue to build their gov­er­nance project.”

    Herbert-​​Spencer laissez-​​faire cor­po­ratism cap­i­tal­ism pol­i­tics con­ser­vatism via:cshalizi

  • BloJJ — About con­fer­ence poster design and defense:

    “My approach is dif­fer­ent. Poster pre­sen­ta­tion, like con­fer­ence pre­sen­ta­tion, belongs more to the area of dra­matic arts than to mar­ket­ing. It is information/​entertainment, and that is the main thing you have to bear in mind when prepar­ing for the ses­sion. Plus, while at a con­fer­ence you have the full atten­tion of your audi­ence (shared, of course, with email, Face­book, plus the 10% that are sim­ply speak­ing) in a poster ses­sion you have to first attract the atten­tion of the peo­ple wan­der­ing around a hall shared with other 20 to 100 posters, then keep them there for the dura­tion of the spiel and while you start a new one, and then, of course, con­vey the infor­ma­tion you want to share with your poster. ”

    advice academic-​​culture meet­ing poster-​​presentaitons skills

  • Economist’s View: The 999

    “Some Indi­vid­u­als of our Coun­try­men, by the Smiles of Prov­i­dence or some other Means, are enabled to roll in their four–wheel’d Car­riages, and can sup­port the Expence of good Houses, rich Fur­ni­ture, and Lux­u­ri­ous Liv­ing. But, is it equi­table that 99, or rather 999 should suf­fer for the Extrav­a­gance or Grandeur of one? Espe­cially when it is consider’d, that Men fre­quently owe their Wealth to the Impov­er­ish­ment of their Neighbours.”

    it-​​was-​​ever-​​thus

  • Ris­ingTide­Har­bor: Matt Barcomb’s Blog on Lean Agile Busi­ness Soft­ware Devel­op­ment: Stop B*tching About Local Optimizations

    “In fact, one approach is to inten­tion­ally over opti­mize a local opti­miza­tion. This will often make appar­ent to man­age­ment (or even to you) where the true bot­tle neck in the sys­tem is. We shouldn’t worry so much about doing the wrong things righter, but we should be aware that that may be the case and always work to be doing the right things. In the end, show­ing improve­ment and build­ing momen­tum can lead to excit­ing changes. In fair­ness, it can also come crash­ing to the ground if the right kinds of changes aren’t made at some point, but this should not deter any­one who thinks some­thing can be made bet­ter from try­ing to do so and it cer­tainly should not be a rea­son to do nothing!”

    change cultural-​​engineering organizational-​​behavior local-​​optimization

  • Geof­frey Chaucer Hath a Blog: A Long Tyme Agoon in a Shire Far Away

    “…A WHINY YOUTHE cam nexte, barl­eye a man, With yelwe haire, tunique, and farmeres tan. But aqua­cul­ture litel did he love, He wolde been a pilot al above And bulls­eye oump-​​rattes yn a nim­ble craft.…”

    amus­ing

  • knitr: Ele­gant, flex­i­ble and fast dynamic report gen­er­a­tion with R | knitr

    “The knitr pack­age was designed to be a trans­par­ent engine for dynamic report gen­er­a­tion with R, solve some long-​​standing prob­lems in Sweave, and com­bine fea­tures in other add-​​on pack­ages into one pack­age (knitr ≈ Sweave + cacheSweave + pgf­Sweave + weaver + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more).”

    R-​​language LaTeX type­set­ting dynamic-​​documents writ­ing tools

  • Math Notes | Futil­ity Closet

    kata for GP

    nudge-​​targets mathematical-​​recreations

  • Cere­bral Mastication

    “There’s a charm­ing lit­tle brain teaser that’s going around the Inter­webs. It’s got var­i­ous forms, but they all look some­thing like this:…”

    nudge-​​targets mathematical-​​recreations

  • Tanya Khovanova’s Math Blog » Blog Archive » Inter­lock­ing Polyominoes

    “A set of poly­omi­noes is inter­locked if no sub­set can be moved far away from the rest. It was known that poly­omi­noes that are built from four or fewer squares do not inter­lock. The project of Dhawan and his men­tor was to inves­ti­gate the inter­locked­ness of larger poly­omi­noes. And they totally deliv­ered. They quickly proved that you can inter­lock poly­omi­noes with eight or more squares. Then they proved that pen­tomi­noes can’t inter­lock. This left them with a gray area: what hap­pens with poly­omi­noes with six or seven squares? After draw­ing many beau­ti­ful pic­tures, they finally found the struc­ture pre­sented in our accom­pa­ny­ing image. The sys­tem con­sists of 12 hex­omi­noes and 5 pen­tomi­noes, and it is rigid. You can­not move a thing. That means that hex­omi­noes can be inter­locked and thus the gray area was resolved.”

    poly­omi­noes mathematical-​​recreations nudge-​​targets

  • Pool based evo­lu­tion­ary algo­rithm pre­sented in EvoStar 2012 « GeNeura Team

    “This is the first inter­na­tion­ally pub­lished paper (it was pre­vi­ously pub­lished in a Span­ish con­fer­ence of a series that deals with a sys­tem, intended for vol­un­teer com­put­ing, that uses a pool for imple­ment­ing dis­trib­uted evo­lu­tion­ary algo­rithms. The basic idea is that the pop­u­la­tion resides in a pool (imple­mented using CouchDB), with clients pulling indi­vid­u­als from the pool, doing stuff on them, and putting them back in the pool. The algo­rithm uses, as much as pos­si­ble, CouchDB fea­tures (such as revi­sions and views) to achieve good per­for­mance. All the code (for this and, right now, for the next papers) is avail­able as open-​​source code.”

    distributed-​​processing evolutionary-​​algorithms CouchDB nudge

  • What Amazon’s ebook strat­egy means — Charlie’s Diary

    “If the major pub­lish­ers switch to sell­ing ebooks with­out DRM, then they can enable cus­tomers to buy books from a vari­ety of out­lets and move away from the walled gar­den of the Kin­dle store. They see DRM as a defense against piracy, but piracy is a much less imme­di­ate threat than a gigan­tic multi­na­tional with rev­enue of $48 Bil­lion in 2011 (more than the entire global pub­lish­ing indus­try) that has expressed its inten­tion to “dis­rupt” them, and whose chief exec­u­tive said recently “even well-​​meaning gate­keep­ers slow inno­va­tion” (where “inno­va­tion” is code-​​speak for “oppor­tu­ni­ties for me to turn a profit”). And so they will deep-​​six their exist­ing com­mit­ment to DRM and use the terms of the DoJ-​​imposed set­tle­ment to wig­gle out of the most-​​favoured-​​nation terms imposed by Ama­zon, in order to sell their wares as widely as pos­si­ble. If they don’t, they’re doomed. And all of us who like to read (or write) fic­tion get to live in the Ama­zon com­pany town.”

    monopoly-​​and-​​monpsony-​​sittin-​​in-​​a-​​tree Ama­zon eBooks disintermediation-​​in-​​action cor­po­ratism redis­in­ter­me­di­a­tion

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  • Wel­come to the Group Pat­tern Lan­guage Project | Group Works

    “This deck of 91 full-​​colour cards names what skilled facil­i­ta­tors and other par­tic­i­pants do to make things work.  The con­tent is more spe­cific than val­ues and less spe­cific than tips and tech­niques, cut­ting across exist­ing method­olo­gies with a designer’s eye to cap­ture the pat­terns that repeat.  The deck can be used to plan sess­sions, reflect on and debrief them, pro­vide guid­ance, and share respon­si­bil­ity for mak­ing the process go well.  It has the poten­tial to pro­vide a com­mon ref­er­ence point for prac­ti­tion­ers, and serve as a frame­work and learn­ing tool for those study­ing the field. ”

    via:bkerr col­lab­o­ra­tion design-​​patterns tools social-​​dynamics

  • [1202.0001] Vector-​​based model of elas­tic bonds for DEM sim­u­la­tion of solids

    “A new model for com­puter sim­u­la­tion of solids, com­posed of bonded par­ti­cles, is pro­posed. Vec­tors rigidly con­nected with par­ti­cles are used for descrip­tion of defor­ma­tion of a sin­gle bond. The expres­sion for poten­tial energy of the bond and cor­re­spond­ing expres­sions for forces and moments are pro­posed. For­mu­las, con­nect­ing para­me­ters of the model with lon­gi­tu­di­nal, shear, bend­ing and tor­sional stiff­nesses of the bond, are derived. It is shown that the model allows to describe any val­ues of the bond stiff­nesses exactly. Two dif­fer­ent cal­i­bra­tion pro­ce­dures depend­ing on bond length/​thickness ratio are pro­posed. It is shown that para­me­ters of model can be cho­sen so that under small defor­ma­tions the bond is equiv­a­lent to either Bernoulli-​​Euler or Tim­o­shenko rod or short cylin­der con­nect­ing par­ti­cles. Sim­ple expres­sions, con­nect­ing para­me­ters of V-​​model with geo­met­ri­cal and mechan­i­cal char­ac­ter­is­tics of the bond, are derived. Com­puter sim­u­la­tion of dynam­i­cal buck­ling of the straight dis­crete rod and dis­crete half-​​spherical shell is car­ried out.”

    mod­el­ing mechanical-​​systems materials-​​science computational-​​methods algo­rithms nudge-​​targets

  • [1202.0253] High-​​speed Flight in an Ergodic Forest

    “Inspired by birds fly­ing through clut­tered envi­ron­ments such as dense forests, this paper stud­ies the the­o­ret­i­cal foun­da­tions of a novel motion plan­ning prob­lem: high-​​speed nav­i­ga­tion through a randomly-​​generated obsta­cle field when only the sta­tis­tics of the obsta­cle gen­er­at­ing process are known a pri­ori. Resem­bling a pla­nar for­est envi­ron­ment, the obsta­cle gen­er­at­ing process is assumed to deter­mine the loca­tions and sizes of disk-​​shaped obsta­cles. When this process is ergodic, and under mild tech­ni­cal con­di­tions on the dynam­ics of the bird, it is shown that the exis­tence of an infi­nite collision-​​free tra­jec­tory through the for­est exhibits a phase tran­si­tion. On one hand, if the bird flies faster than a cer­tain crit­i­cal speed, then, with prob­a­bil­ity one, there is no infi­nite collision-​​free tra­jec­tory, i.e., the bird will even­tu­ally col­lide with some tree, almost surely, regard­less of the plan­ning algo­rithm gov­ern­ing the bird’s motion. On the other hand, if the bird flies slower than this crit­i­cal speed, then there exists at least one infi­nite collision-​​free tra­jec­tory, almost surely. Lower and upper bounds on the crit­i­cal speed are derived for the spe­cial case of a homo­ge­neous Pois­son for­est con­sid­er­ing a sim­ple model for the bird’s dynam­ics. For the same case, an equiv­a­lent per­co­la­tion model is pro­vided. Using this model, the phase dia­gram is approx­i­mated in Monte-​​Carlo sim­u­la­tions. This paper also estab­lishes novel con­nec­tions between robot motion plan­ning and sta­tis­ti­cal physics through ergodic the­ory and per­co­la­tion the­ory, which may be of inde­pen­dent interest.”

    robot­ics plan­ning algo­rithms nudge-​​targets

  • [1202.0077] An Inter­act­ing Par­ti­cle Model for Clus­ter­ing Euclid­ean Datasets

    “In this paper we pro­pose a method based on inter­act­ing par­ti­cle physics, devised for clus­ter­ing Euclid­ean datasets with­out ini­tial con­straints or con­di­tions. We model any dataset as an inter­act­ing par­ti­cle sys­tem, whose ele­ments cor­re­spond to par­ti­cles that inter­act through a sim­pli­fied ver­sion of Lennard-​​Jones poten­tials. In so doing, mutual attrac­tive inter­ac­tions allow to iden­tify groups of prox­i­mal par­ti­cles. The main out­come of this mod­el­ing task is an adja­cency matrix, taken as input by a com­mu­nity detec­tion algo­rithm aimed to iden­tify dif­fer­ent par­ti­tions. The under­ly­ing con­jec­ture is that, using a mul­tires­o­lu­tion analy­sis, the adopted model allows to find the right num­ber of clus­ters for any given dataset. Exper­i­men­tal results, per­formed in com­par­i­son with a clas­si­cal clus­ter­ing algo­rithm, con­firm this assumption.”

    clus­ter­ing data-​​analysis algo­rithms nudge-​​targets distributed-​​processing

  • [1201.6583] Empow­er­ment for Con­tin­u­ous Agent-​​Environment Systems

    “This paper devel­ops gen­er­al­iza­tions of empow­er­ment to con­tin­u­ous states. Empow­er­ment is a recently intro­duced information-​​theoretic quan­tity moti­vated by hypothe­ses about the effi­ciency of the sen­so­ri­mo­tor loop in bio­log­i­cal organ­isms, but also from con­sid­er­a­tions stem­ming from curiosity-​​driven learn­ing. Empowe­mer­ment mea­sures, for agent-​​environment sys­tems with sto­chas­tic tran­si­tions, how much influ­ence an agent has on its envi­ron­ment, but only that influ­ence that can be sensed by the agent sen­sors. It is an information-​​theoretic gen­er­al­iza­tion of joint con­trol­la­bil­ity (influ­ence on envi­ron­ment) and observ­abil­ity (mea­sure­ment by sen­sors) of the envi­ron­ment by the agent, both con­trol­la­bil­ity and observ­abil­ity being usu­ally defined in con­trol the­ory as the dimen­sion­al­ity of the control/​observation spaces.…”

    agent-​​based emergent-​​design robot­ics engineering-​​design machine-​​learning empow­er­ment nudge

  • [1201.6655] Learn­ing Per­for­mance of Pre­dic­tion Mar­kets with Kelly Bettors

    “In eval­u­at­ing pre­dic­tion mar­kets (and other crowd-​​prediction mech­a­nisms), inves­ti­ga­tors have repeat­edly observed a so-​​called “wis­dom of crowds” effect, which roughly says that the aver­age of par­tic­i­pants per­forms much bet­ter than the aver­age par­tic­i­pant. The mar­ket price—an aver­age or at least aggre­gate of traders’ beliefs—offers a bet­ter esti­mate than most any indi­vid­ual trader’s opin­ion. In this paper, we ask a stronger ques­tion: how does the mar­ket price com­pare to the best trader’s belief, not just the aver­age trader. We mea­sure the market’s worst-​​case log regret, a notion com­mon in machine learn­ing the­ory. To arrive at a mean­ing­ful answer, we need to assume some­thing about how traders behave. We sup­pose that every trader opti­mizes accord­ing to the Kelly cri­te­ria, a strat­egy that prov­ably max­i­mizes the com­pound growth of wealth over an (infi­nite) sequence of mar­ket inter­ac­tions. We show sev­eral consequences.…”

    pre­dic­tion performance-​​measure agent-​​based sim­u­la­tion nudge-​​targets wisdom-​​of-​​crowds

  • Curat­ing the kraken « Pub­lic Historian

    ‘This is why “curate” is still a word to con­jure by in our cul­ture.  It still promises trans­for­ma­tive power.’

    muse­ol­ogy prag­mat­ics nam­ing engineering-​​of-​​philosophy

  • [1201.5780] Full and Half Gilbert Tes­sel­la­tions with Rec­tan­gu­lar Cells

    “We inves­ti­gate the ray-​​length dis­tri­b­u­tions for two dif­fer­ent rec­tan­gu­lar ver­sions of Gilbert’s tes­sel­la­tion. In the full rec­tan­gu­lar ver­sion, lines extend either hor­i­zon­tally (with east– and west-​​growing rays) or ver­ti­cally (north– and south-​​growing rays) from seed points which form a Pois­son point process, each ray stop­ping when another ray is met. In the half rec­tan­gu­lar ver­sion, east and south grow­ing rays do not inter­act with west and north rays. For the half rec­tan­gu­lar tes­sel­la­tion we com­pute ana­lyt­i­cally, via recur­sion, a series expan­sion for the ray-​​length dis­tri­b­u­tion, whilst for the full rec­tan­gu­lar ver­sion we develop an accu­rate sim­u­la­tion tech­nique, based in part on the stopping-​​set the­ory of Zuyev, to accom­plish the same. We demon­strate the remark­able fact that plots of the two dis­tri­b­u­tions appear to be iden­ti­cal when the inten­sity of seeds in the half model is twice that in the full model. Our paper explores this coin­ci­dence mind­ful of the fact that, for one model, our results are from a sim­u­la­tion (with inher­ent sam­pling error).…”

    geom­e­try tiling algo­rithms generative-​​art sim­u­la­tion emer­gence interesting-​​problem

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  • [1201.5604] Dis­crete and Fuzzy Dynam­i­cal Genetic Pro­gram­ming in the XCSF Learn­ing Clas­si­fier System

    “A num­ber of rep­re­sen­ta­tion schemes have been pre­sented for use within Learn­ing Clas­si­fier Sys­tems, rang­ing from binary encod­ings to neural net­works. This paper presents results from an inves­ti­ga­tion into using dis­crete and fuzzy dynam­i­cal sys­tem rep­re­sen­ta­tions within the XCSF Learn­ing Clas­si­fier Sys­tem. In par­tic­u­lar, asyn­chro­nous Ran­dom Boolean Net­works are used to rep­re­sent the tra­di­tional condition-​​action pro­duc­tion sys­tem rules in the dis­crete case and asyn­chro­nous Fuzzy Logic Net­works in the continuous-​​valued case. It is shown pos­si­ble to use self-​​adaptive, open-​​ended evo­lu­tion to design an ensem­ble of such dynam­i­cal sys­tems within XCSF to solve a num­ber of well-​​known test problems.”

    Kauffman-​​networks learning-​​classifier-​​systems genetic-​​programming nudge-​​targets inter­est­ing

  • [1201.4899] I Like Her more than You: Self-​​determined Communities

    “In this paper we define what we call an affin­ity sys­tem, which is a set of indi­vid­u­als, each with a vec­tor char­ac­ter­iz­ing its pref­er­ence for all other indi­vid­u­als in the set. The pref­er­ence of a mem­ber can be given either by a rank­ing of all mem­bers or by a weighted vec­tor that defines the degrees of its affin­ity to oth­ers. Affin­ity sys­tems are use­ful for mod­el­ing social sys­tems as well as gen­eral data sets, as social inter­ac­tions are often deter­mined by affini­ties among the mem­bers. We also define a nat­ural notion of (poten­tially over­lap­ping) com­mu­ni­ties in an affin­ity sys­tem, in which the mem­bers of a given com­mu­nity col­lec­tively pre­fer each other to any­one else out­side the com­mu­nity. Thus these com­mu­ni­ties are “self-​​determined” or “self-​​certified” by the affin­ity sys­tem. We pro­vide a tight poly­no­mial bound on the num­ber of self-​​determined com­mu­ni­ties as a func­tion of the robust­ness of the com­mu­nity. More­over, we present a polynomial-​​time algo­rithm for enu­mer­at­ing these com­mu­ni­ties, as well as a local algo­rithm with a strong sto­chas­tic per­for­mance guar­an­tee that can find a com­mu­nity in time nearly lin­ear in the of size the community.…”

    network-​​theory social-​​capital social-​​dynamics self-​​assembly agent-​​based graph-​​theory algo­rithms com­plex­ol­ogy nudge-​​targets

  • [1201.5076] Tech­ni­cal Report #SEHIR-IE-VA-12–1: Opti­mal Obsta­cle Place­ment with Disambiguations

    “We intro­duce the opti­mal obsta­cle place­ment with dis­am­bigua­tions prob­lem wherein the goal is to place true obsta­cles in an envi­ron­ment clut­tered with false obsta­cles so as to max­i­mize the total tra­ver­sal length of a nav­i­gat­ing agent (NAVA). Prior to the tra­ver­sal, NAVA is given loca­tion infor­ma­tion and prob­a­bilis­tic esti­mates of each disk-​​shaped hin­drance (here­inafter referred to as disk) being a true obsta­cle. The NAVA can dis­am­biguate a disk’s sta­tus only when sit­u­ated on its bound­ary. There exists an obsta­cle plac­ing agent (OPA) that locates obsta­cles prior to NAVA’s tra­ver­sal. The goal of OPA is to place true obsta­cles in between the clut­ter in such a way that NAVA’s tra­ver­sal length is max­i­mized in a game-​​theoretic sense.…”

    agent-​​based game-​​theory robot­ics disambiguation-​​design nudge-​​targets military-​​applications algo­rithms

  • [1010.5017] Col­lec­tive motion

    “We review the obser­va­tions and the basic laws describ­ing the essen­tial aspects of col­lec­tive motion — being one of the most com­mon and spec­tac­u­lar man­i­fes­ta­tion of coor­di­nated behav­ior. Our aim is to pro­vide a bal­anced dis­cus­sion of the var­i­ous facets of this highly mul­ti­dis­ci­pli­nary field, includ­ing exper­i­ments, math­e­mat­i­cal meth­ods and mod­els for sim­u­la­tions, so that read­ers with a vari­ety of back­ground could get both the basics and a broader, more detailed pic­ture of the field. The obser­va­tions we report on include sys­tems con­sist­ing of units rang­ing from macro­mol­e­cules through metal­lic rods and robots to groups of ani­mals and peo­ple. Some empha­sis is put on mod­els that are sim­ple and real­is­tic enough to repro­duce the numer­ous related obser­va­tions and are use­ful for devel­op­ing con­cepts for a bet­ter under­stand­ing of the com­plex­ity of sys­tems con­sist­ing of many simul­ta­ne­ously mov­ing enti­ties. As such, these mod­els allow the estab­lish­ing of a few fun­da­men­tal prin­ci­ples of flock­ing. In par­tic­u­lar, it is demon­strated, that in spite of con­sid­er­able dif­fer­ences, a num­ber of deep analo­gies exist between equi­lib­rium sta­tis­ti­cal physics sys­tems and those made of self-​​propelled (in most cases liv­ing) units. In both cases only a few well defined macroscopic/​collective states occur and the tran­si­tions between these states fol­low a sim­i­lar sce­nario, involv­ing dis­con­ti­nu­ity and alge­braic divergences.”

    emer­gence emergent-​​design biol­ogy ethol­ogy com­plex­ol­ogy mod­els artificial-​​life nudge-​​targets

  • [1201.5568] Dynamic trees for stream­ing and mas­sive data contexts

    “Data col­lec­tion at a mas­sive scale is becom­ing ubiq­ui­tous in a wide vari­ety of set­tings, from vast offline data­bases to stream­ing real-​​time infor­ma­tion. Learn­ing algo­rithms deployed in such con­texts must rely on single-​​pass infer­ence, where the data his­tory is never revis­ited. In stream­ing con­texts, learn­ing must also be tem­po­rally adap­tive to remain up-​​to-​​date against unfore­seen changes in the data gen­er­at­ing mech­a­nism. Although rapidly grow­ing, the online Bayesian infer­ence lit­er­a­ture remains chal­lenged by mas­sive data and tran­sient, evolv­ing data streams. Non-​​parametric mod­el­ling tech­niques can prove par­tic­u­larly ill-​​suited, as the com­plex­ity of the model is allowed to increase with the sam­ple size. In this work, we take steps to over­come these chal­lenges by port­ing stan­dard stream­ing tech­niques, like data dis­card­ing and down­weight­ing, into a fully Bayesian frame­work via the use of infor­ma­tive pri­ors and active learn­ing heuris­tics. We show­case our meth­ods by aug­ment­ing a mod­ern non-​​parametric mod­el­ling frame­work, dynamic trees, and illus­trate its per­for­mance on a num­ber of prac­ti­cal exam­ples. The end prod­uct is a pow­er­ful stream­ing regres­sion and clas­si­fi­ca­tion tool, whose per­for­mance com­pares favourably to the state-​​of-​​the-​​art.”

    data-​​analysis learning-​​from-​​data algo­rithms drinking-​​from-​​the-​​firehose nudge data-​​mining