Tag Archives: diversity

Items of some interest:

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

  • Altaeros Ener­gies Releases Demo Video of Their Fly­ing Wind Tur­bine — Core77

    “What you saw there was a scaled pro­to­type, 35 feet in diam­e­ter. Dur­ing the test run it arrived on-​​site in a dock attached to a trailer, then deployed, acti­vated the tur­bine, and returned to the ground—all auto­mat­i­cally. At its high­est alti­tude of 350 feet, it suc­cess­fully got the tur­bine to gen­er­ate twice as much juice than it gets at tower height. We’d say Altaeros is one to watch.”

    wind-​​power pro­to­type engineering-​​design sus­tain­abil­ity energy

  • More on DRM and ebooks — Charlie’s Diary

    “There is a per­va­sive assump­tion that ebooks are dis­pos­able lit­er­a­ture. But to the vora­cious read­ers, this is not the case. Cur­rently it’s hard for many peo­ple to build up col­lec­tions of books due to space con­straints — nev­er­the­less I know many SF fans (of the kind who read 50–150 books a year) who have turned their homes into libraries. They will be the tip of an ice­berg once ebooks become main­stream; why dis­card an ebook when you can file it and come back to it in 10 years’ time and it takes up no space? For such peo­ple, fil­ing and tag­ging their col­lec­tions is a major issue. And so is porta­bil­ity. It’s true that if they own an iPad they can have an iBooks app full of books pur­chased from Apple, and a Kin­dle app full of books from Ama­zon, and a Nook app full of books from B&N. But those apps are, thanks to DRM, data silos — you can’t cross-​​check to see if you bought book 3 in a series from Apple and book 5 from Ama­zon with­out a lot of fid­dling around.”

    business-​​opportunity col­lect­ing nerds-​​as-​​camel-​​nose my-​​people vague-​​press

  • Avería – The Aver­age Font

    “I am not a type designer. This is the story of the cre­ation of a new font, Avería: the aver­age of all the fonts on my com­puter. The field of typog­ra­phy has long fas­ci­nated me, and I love play­ing with cre­ative pro­gram­ming ideas, so it was per­haps inevitable that the idea came to me one day of “gen­er­a­tive typog­ra­phy”. A Google on the sub­ject brought up lit­tle, and I put the idea to the back of my mind until it occurred to me that per­haps the process of aver­ag­ing, or inter­po­lat­ing, exist­ing fonts might bring up inter­est­ing results. Luck­ily at this point I didn’t do any more web search­ing – instead I grabbed my lap­top and came up with an ini­tial idea for find­ing what the aver­age of all my fonts might look like – by over­lay­ing each let­ter at low opac­ity. The results can be seen in the below image.”

    typog­ra­phy type-​​design type­face generative-​​art design graphic-​​design

  • Phillip Rhodes’ Weblog

    “In short, it’s time for a res­ur­rec­tion of the crypto-​​anarchist /​ techno-​​libertarian /​ cypher­punk move­ment and it’s asso­ci­ated val­ues, activ­i­ties and aes­thetic. Those of us who care about these issues can’t just lurk in the shad­ows and act like noth­ing is hap­pen­ing. It’s time to start telling peo­ple about public-​​key encryp­tion, host­ing key-​​signing par­ties, devel­op­ing new tech­nolo­gies for bypass­ing Inter­net cen­sor­ship, devel­op­ing tools for bypass­ing State and Cor­po­ra­tion con­trolled mes­sag­ing chan­nels, and tak­ing a stand for freedom.”

    cryp­tog­ra­phy nrrrrds cultural-​​assumptions cultural-​​dynamics diver­sity

  • No, physi­cians don’t under­stand screen­ing sta­tis­tics | The Inci­den­tal Economist

    “So basically,when it comes to sav­ing lives, docs are three times more likely to rec­om­mend a screen­ing test based on irrel­e­vant data than they are to rec­om­mend it based on rel­e­vant data. I’m brac­ing myself for the hate mail, but this is part of the rea­son why I’m skep­ti­cal that just pro­vid­ing docs with more evi­dence will change the way they prac­tice. Most docs just aren’t trained to under­stand this stuff.”

    medical-​​culture health­care sta­tis­tics probability-​​theory plan­ning

  • reveal.js


    slides pre­sen­ta­tion library javascript sim­ple

  • Fancy HTML5 Slides with knitr and pan­doc | Yihui Xie

    “Karthik Ram gave an Intro­duc­tion to R a cou­ple of weeks ago, and I strongly rec­om­mend you to take a look at his cool HTML5 slides. I started try­ing HTML5 slides last year, and now it is dif­fi­cult for me to go back to beamer, which I have used for a few years for my pre­se­n­a­tions. It is hor­ri­ble to see beamer slides every­where at aca­d­e­mic con­fer­ences (espe­cially the clas­sic blue themes).”

    slides pre­sen­ta­tion library javascript mark­down

  • An algo­rithm is just an algo­rithm | Gene Expres­sion | Dis­cover Magazine

    “Another illus­tra­tion that knowl­edge comes not through blind adher­ence to meth­ods, but human reflection.”

    algo­rithms sta­tis­tics sto­ry­telling i-​​need-​​the-​​name-​​for-​​this

  • Cap­tur­ing dealer descrip­tions in our online cat­a­log — Yale Law Library — Rare Books Blog

    “Attrac­tive and rare set of decrees con­cern­ing the func­tion­ing of the judi­ciary in the papal city of Bologna. These city statutes were pro­mul­gated by the Pope’s legate, Car­di­nal Benedetto Gius­tini­ani (1554−1621). Despite the issu­ing author­ity, the con­sti­tu­tions (a word indi­cat­ing leg­is­la­tion of the high­est level) are entirely non-​​religious in con­tent, relat­ing to civil law jus­tice in the city. They shed con­sid­er­able light into how courts worked in Bologna. Included are instruc­tions on cases involv­ing poor peo­ple; rules for notaries; the keep­ing of reg­is­ters; seizures of prop­erty; tak­ing of sus­pects; pay­ment of offi­cers; expert wit­nesses; and the gov­ern­ing of appeals. Pages 192–198 com­prise papal edicts on the salaries of Bolog­nese judges and notaries.” — Leo Cado­gan Rare Books (Dec. 2011)

    books cat­a­log nanohis­tory librar­i­ans meta­data

Items of some interest:

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

  • Jonathan Lethem’s ‘Neote­nous Aes­thetic” — Mis­ter Bit — Wired​.it

    ‘Dur­ing the brief, but very inter­est­ing Q&A ses­sion, Lethem argued that inter­net cul­ture brought the “closet into the open”, that is, it gave ephemera, triv­i­al­i­ties, and every­day activ­i­ties “A new kind of vis­i­bil­ity”. “Peo­ple have always been pro­duc­ing weird stuff and have always been engag­ing in arcane activ­i­ties,” Lethem remarked. “What is really new is the fact the now we can see it. We can see it all. We can quan­tify what we do — or not do — online.” Lethem men­tioned the uncanny abil­ity to track, in real time, “how many books I am not sell­ing on Ama­zon”. “Real­ity has acquired a new level of mea­sur­a­bil­ity”. “The activ­i­ties we per­form in our dig­i­tal age are not nec­es­sar­ily new. What is new is that. We. Can. See. Them. All.”.’

    one-​​measures-​​a-​​circle inter­per­me­ation access local­ism

  • Sex, Oil, and Video­tape | Mother Jones

    “Loom­ing over Saylor’s con­fronta­tion with Bolen­baugh was the EPA’s Sep­tem­ber 27 cleanup dead­line, and it appears that Enbridge and its con­trac­tors were feel­ing the pres­sure as it drew near. In early Sep­tem­ber, after the Michi­gan Mes­sen­ger pub­lished its exposé on the use of undoc­u­mented work­ers by Hall­mark Indus­trial, another group of work­ers employed by a dif­fer­ent Enbridge con­trac­tor came for­ward with detailed sto­ries of how they had been instructed to con­ceal oil at the same site. Work­ers would land on an island, they said, remove all veg­e­ta­tion, and then lay out absorbent pom-​​poms, all per EPA reg­u­la­tions. But once the top layer of oil was absorbed, they were instructed to rake dirt over the area to make it appear as though it had been dug out. One worker described his super­vi­sor show­ing him the process step-​​by-​​step, con­clud­ing with sprin­kling a thin layer of dirt on top. “He said, ‘There, now they can’t see it. It is clean,’” the worker told the Mes­sen­ger. Another worker described being told to cover pock­ets of oil with leaves and sticks. As a last step, such areas were cor­doned off with cau­tion tape.”

    oil­spill Kala­ma­zoo local whistle­blower

  • [1204.4200] Dis­crete Dynam­i­cal Genetic Pro­gram­ming in XCS

    “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 a dis­crete dynam­i­cal sys­tem rep­re­sen­ta­tion within the XCS Learn­ing Clas­si­fier Sys­tem. In par­tic­u­lar, asyn­chro­nous ran­dom Boolean net­works are used to rep­re­sent the tra­di­tional condition-​​action pro­duc­tion sys­tem rules. It is shown pos­si­ble to use self-​​adaptive, open-​​ended evo­lu­tion to design an ensem­ble of such dis­crete dynam­i­cal sys­tems within XCS to solve a num­ber of well-​​known test problems.”

    genetic-​​programming learning-​​classifier-​​systems representation-​​theory design-​​patterns boolean-​​networks nudge-​​targets nice

  • Why Is Darwin’s Tan­gled Bank Tan­gled? : 13.7: Cos­mos And Cul­ture : NPR

    Sad to hear him still phras­ing this sim­ple truth so obscurely: Not “Because, on the scale of mol­e­c­u­lar bind­ing site recog­ni­tion, say a few tens of angstroms in length, height and width and sev­eral other fea­tures such as polar­ity, van-​​der-​​Waal forces, and so on, there are far fewer effec­tively dif­fer­ent mol­e­c­u­lar shapes than there are kinds of mol­e­cules.“ … but “Because there are fewer sto­ries than there are facts.”

    oh-​​stu pragmatism-it-ain’t philosophy-​​of-​​science

  • Math Notes | Futil­ity Closet

    So for finite sequences of dig­its, which sequences are such that the most right-​​truncated sub­strings are prime? Which are such that the most right-​​repeating exten­sions are prime?

    nudge-​​targets number-​​theory indirect-​​link

  • Home — Scal­able and Mod­u­lar Archi­tec­ture for CSS

    “I’ve been ana­lyz­ing my process (and the process of those around me) and fig­ur­ing out how best to struc­ture code for projects on a larger scale. What I’ve found is a process that works equally well for sites small and large. Learn how to struc­ture your CSS to allow for flex­i­bil­ity and main­tain­abil­ity as your project and your team grows.”

    css tuto­r­ial best-​​practices graphic-​​design via-​​trek

  • [1204.3678] Crowd Mem­ory: Learn­ing in the Collective

    “Crowd algo­rithms often assume work­ers are inex­pe­ri­enced and thus fail to adapt as work­ers in the crowd learn a task. These assump­tions fun­da­men­tally limit the types of tasks that sys­tems based on such algo­rithms can han­dle. This paper explores how the crowd learns and remem­bers over time in the con­text of human com­pu­ta­tion, and how more real­is­tic assump­tions of worker expe­ri­ence may be used when design­ing new sys­tems. We first demon­strate that the crowd can recall infor­ma­tion over time and dis­cuss pos­si­ble impli­ca­tions of crowd mem­ory in the design of crowd algo­rithms. We then explore crowd learn­ing dur­ing a con­tin­u­ous con­trol task. Recent sys­tems are able to dis­guise dynamic groups of work­ers as crowd agents to sup­port con­tin­u­ous tasks, but have not yet con­sid­ered how such agents are able to learn over time. We show, using a real-​​time gam­ing set­ting, that crowd agents can learn over time, and ‘remem­ber’ by pass­ing strate­gies from one gen­er­a­tion of work­ers to the next, despite high turnover rates in the work­ers com­pris­ing them. We con­clude with a dis­cus­sion of future research direc­tions for crowd mem­ory and learning.”

    crowd­sourc­ing learn­ing agent-​​based collective-​​intelligence mem­ory nudge-​​targets

  • [0911.1582] Manip­u­lat­ing Tour­na­ments in Cup and Round Robin Competitions

    “In sports com­pe­ti­tions, teams can manip­u­late the result by, for instance, throw­ing games. We show that we can decide how to manip­u­late round robin and cup com­pe­ti­tions, two of the most pop­u­lar types of sport­ing com­pe­ti­tions in poly­no­mial time. In addi­tion, we show that find­ing the min­i­mal num­ber of games that need to be thrown to manip­u­late the result can also be deter­mined in poly­no­mial time. Finally, we show that there are sev­eral dif­fer­ent vari­a­tions of stan­dard cup com­pe­ti­tions where manip­u­la­tion remains polynomial.”

    algo­rithms eco­nom­ics game-​​theory nudge-​​targets

  • Intro­duc­ing the Jour­nal of Dig­i­tal Human­i­ties — ProfHacker — The Chron­i­cle of Higher Education

    “If the con­tents of the inau­gural issue—which range from an essay argu­ing that human­ists need to under­stand and inter­pret quan­ti­ta­tive data to a review of the Word­Seer text analy­sis tool—fall out­side your usual schol­arly domain, then cer­tainly the journal’s edi­to­r­ial and pub­lish­ing appa­ra­tus will piqué your interest. As Dan Cohen explained in a sep­a­rate blog post, the jour­nal oper­ates under the model of catch­ing the good—of find­ing sub­stan­tive and valu­able dig­i­tal human­i­ties work “in what­ever for­mat, and wher­ever, it exists.” Blogs, pod­casts, Twit­ter con­ver­sa­tions, slideshows, and so on, these are all venues in which sig­nif­i­cant and, though I hate to use such an ungainly word, impact­ful work is being done. The reg­u­lar and guest edi­tors “catch” this work, and then pro­vide lay­ers of eval­u­a­tion and review before it appears in JDH.”

    digital-​​humanities jour­nal to-​​read two-​​cultures-​​only-​​one-​​of-​​which-​​can-​​write

  • [1005.4159] The Com­plex­ity of Manip­u­lat­ing $k$-Approval Elections

    “An impor­tant prob­lem in com­pu­ta­tional social choice the­ory is the com­plex­ity of unde­sir­able behav­ior among agents, such as con­trol, manip­u­la­tion, and bribery in elec­tion sys­tems. These kinds of vot­ing strate­gies are often tempt­ing at the indi­vid­ual level but dis­as­trous for the agents as a whole. Cre­at­ing elec­tion sys­tems where the deter­mi­na­tion of such strate­gies is dif­fi­cult is thus an impor­tant goal. …”

    vot­ing game-​​theory design-​​patterns mechanism-​​design nudge-​​targets

  • [0903.1147] Tetravex is NP-​​complete

    “Tetravex is a widely played one per­son com­puter game in which you are given $n^2$ unit tiles, each edge of which is labelled with a num­ber. The objec­tive is to place each tile within a $n$ by $n$ square such that all neigh­bour­ing edges are labelled with an iden­ti­cal num­ber. Unfor­tu­nately, play­ing Tetravex is com­pu­ta­tion­ally hard. More pre­cisely, we prove that decid­ing if there is a tiling of the Tetravex board is NP-​​complete. Decid­ing where to place the tiles is there­fore NP-​​hard. This may help to explain why Tetravex is a good puz­zle. This result com­pli­ments a num­ber of sim­i­lar results for one per­son games involv­ing tiling. For exam­ple, NP-​​completeness results have been shown for: the offline ver­sion of Tetris, KPlumber (which involves rotat­ing tiles con­tain­ing draw­ings of pipes to make a con­nected net­work), and short­est slid­ing puz­zle prob­lems. It raises a num­ber of open ques­tions. For exam­ple, is the infi­nite ver­sion Turing-​​complete? How do we gen­er­ate Tetravex prob­lems which are truly puz­zling as ran­dom NP-​​complete prob­lems are often sur­pris­ing easy to solve? Can we observe phase tran­si­tion behav­iour? What about the com­plex­ity of the prob­lem when it is guar­an­teed to have an unique solu­tion? How do we gen­er­ate puz­zles with unique solutions?”

    mathematical-​​recreations computational-​​complexity algo­rithms nudge-​​targets

  • [1204.4286] Fair Allo­ca­tion With­out Trade

    “We con­sider the age-​​old prob­lem of allo­cat­ing items among dif­fer­ent agents in a way that is effi­cient and fair. Two papers, by Dolev et al. and Ghodsi et al., have recently stud­ied this prob­lem in the con­text of com­puter sys­tems. Both papers had sim­i­lar mod­els for agent pref­er­ences, but advo­cated dif­fer­ent notions of fair­ness. We for­mal­ize both fair­ness notions in eco­nomic terms, extend­ing them to apply to a larger fam­ily of util­i­ties. Not­ing that in set­tings with such util­i­ties effi­ciency is eas­ily achieved in mul­ti­ple ways, we study notions of fair­ness as cri­te­ria for choos­ing between dif­fer­ent effi­cient allo­ca­tions. Our tech­ni­cal results are algo­rithms for find­ing fair allo­ca­tions cor­re­spond­ing to two fair­ness notions: Regard­ing the notion sug­gested by Ghodsi et al., we present a polynomial-​​time algo­rithm that com­putes an allo­ca­tion for a gen­eral class of fair­ness notions, in which their notion is included. For the other, sug­gested by Dolev et al., we show that a com­pet­i­tive mar­ket equi­lib­rium achieves the desired notion of fair­ness, thereby obtain­ing a polynomial-​​time algo­rithm that com­putes such a fair allo­ca­tion and solv­ing the main open prob­lem raised by Dolev et al.”

    eco­nom­ics game-​​theory fair­ness algo­rithms phi­los­o­phy design-​​patterns

  • Why is Esti­mat­ing so Hard? | 8th Light

    “It turns out that we don’t know the pro­ce­dure. We haven’t got any clue to just how dif­fi­cult the pro­ce­dure is. We aren’t com­put­ers. We don’t fol­low pro­ce­dures. And so com­par­ing the com­plex­ity of the man­ual task, to the com­plex­ity of the pro­ce­dure is invalid. This is one of the rea­sons that esti­mates are so hard, and why we get them wrong so often. We look at a task that seems easy and esti­mate it on that basis, only to find that writ­ing down the pro­ce­dure is actu­ally quite intri­cate. We blow the esti­mate because we esti­mate the wrong thing.”

    esti­ma­tion agile-​​practices philosophy-​​of-​​engineering man­age­ment self-​​definition plan­ning

  • [1204.4374] Higher Order City Voronoi Diagrams

    “We inves­ti­gate higher-​​order Voronoi dia­grams in the city met­ric. This met­ric is induced by quick­est paths in the L1 met­ric in the pres­ence of an accel­er­at­ing trans­porta­tion net­work of axis-​​parallel line segments. …”

    computational-​​geometry algo­rithms voronoi-​​diagrams diver­sity network-​​theory nudge-​​targets

  • Topic mod­el­ing made just sim­ple enough. | The Stone and the Shell

    “Com­puter sci­en­tists make LDA seem com­pli­cated because they care about prov­ing that their algo­rithms work. And the proof is indeed brain-​​squashingly hard. But the prac­tice of topic mod­el­ing makes good sense on its own, with­out proof, and does not require you to spend even a sec­ond think­ing about “Dirich­let dis­tri­b­u­tions.” When the math is approached in a prac­ti­cal way, I think human­ists will find it easy, intu­itive, and empow­er­ing. This post focuses on LDA as short­hand for a broader fam­ily of “prob­a­bilis­tic” tech­niques. I’m going to ask how they work, what they’re for, and what their lim­its are.”

    text-​​processing clas­si­fi­ca­tion algo­rithms lovely two-​​cultures-​​only-​​one-​​of-​​which-​​can-​​write

  • Math­e­mati­cians are Giraffe Hunters by Barry Mazur | berfrois

    “No won­der life (i.e., the thing that my once 10-​​year old niece referred to as “the thing that isn’t fair”) comes to us as a fil­i­gree of ash sto­ries. Walk­ing down the street past a cou­ple in con­ver­sa­tion, an over­heard mor­pheme, a mere glance at a wrongly but­toned rain­coat, sparks a nar­ra­tive in our imag­i­na­tion. Ask any ques­tion begin­ning with “why?” and the answer will surely be a story, or it will be embed­ded in a story. Or, at the very least, it will offer a tempt­ing thread for some story that you your­self will hold onto, embell­ish even, as you try to absorb the answer. We inter­po­late between such frag­ments. This is, for many of us, sim­ply the way we think. What about the “why ques­tions” in sci­ence, in logic, in math­e­mat­ics? We should acknowl­edge how they are often “what ques­tions” or “how ques­tions” in dis­guise. Or how they slide down into such ques­tions, as the ever-​​elusive, ever-​​illusory quest for an X that actu­ally causes a Y dis­solves. Some of the more sat­is­fy­ing answers to sci­en­tific “why” ques­tions involves deft rephras­ing. “Why is the sky blue?” is replaced by the ques­tion “what is the func­tion that describes scat­ter­ing ampli­tude as depen­dent on wave-​​length”?”

    math­e­mat­ics philosophy-​​of-​​mathematics sto­ry­telling prag­ma­tism theory-​​and-​​practice-​​sitting-​​in-​​a-​​tree what-​​is-​​it-​​good-​​for-​​hunh

Items of some interest:

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

  • Edge Per­spec­tives with John Hagel: Finite and Infi­nite Games — Which Game Shall We Play in the New Year?

    Far bet­ter, if pos­si­ble, to avoid direct con­fronta­tion and find ways to pur­sue infi­nite game play on the mar­gins or edges of finite game insti­tu­tions or in the white spaces not yet occu­pied by finite game insti­tu­tions.  By draw­ing atten­tion to hori­zons that have not yet been explored and demon­strat­ing the abil­ity to make progress in draw­ing out more poten­tial and pos­si­bil­ity, infi­nite game play­ers have a greater chance of shift­ing the game and attract­ing other play­ers. By build­ing par­al­lel insti­tu­tions and prac­tices that pull oth­ers into their game, infi­nite game play­ers can attract enough crit­i­cal mass so that they can pur­sue their quests with lower risk of inter­ven­tion from the finite game play­ers who view such actions as deeply sub­ver­sive.  At our research cen­ter, JSB and I are now explor­ing these kinds of approaches as a way of achiev­ing orga­ni­za­tional change within large institutions.

    what-​​I-​​do

  • [1107.0056] Fixed para­me­ter algo­rithms for restricted col­or­ing problems

    In this paper, we obtain poly­no­mial time algo­rithms to deter­mine the acyclic chro­matic num­ber, the star chro­matic num­ber, the Thue chro­matic num­ber, the har­mo­nious chro­matic num­ber and the clique chro­matic num­ber of $P_4$-tidy graphs and $(q,q-4)$-graphs, for every fixed $q$. These classes include cographs, $P_4$-sparse and $P_4$-lite graphs. All these col­or­ing prob­lems are known to be NP-​​hard for gen­eral graphs. These algo­rithms are fixed para­me­ter tractable on the para­me­ter $q(G)$, which is the min­i­mum $q$ such that $G$ is a $(q,q-4)$-graph. We also prove that every con­nected $(q,q-4)$-graph with at least $q$ ver­tices is 2-​​clique-​​colorable and that every acyclic col­or­ing of a cograph is also nonrepetitive.

    algo­rithms graph-​​theory discrete-​​mathematics nudge-​​targets

  • [1112.6045] Com­par­ing inter­mit­tency and net­work mea­sure­ments of words and their depen­dency on authorship

    Many fea­tures from texts and lan­guages can now be inferred from sta­tis­ti­cal analy­ses using con­cepts from com­plex net­works and dynam­i­cal sys­tems. In this paper we quan­tify how topo­log­i­cal prop­er­ties of word co-​​occurrence net­works and inter­mit­tency (or bursti­ness) in word dis­tri­b­u­tion depend on the style of authors. Our data­base con­tains 40 books from 8 authors who lived in the 19th and 20th cen­turies, for which the fol­low­ing net­work mea­sure­ments were obtained: clus­ter­ing coef­fi­cient, aver­age short­est path lengths, and between­ness. We found that the two fac­tors with stronger depen­dency on the authors were the skew­ness in the dis­tri­b­u­tion of word inter­mit­tency and the aver­age short­est paths. Other fac­tors such as the betwee­ness and the Zipf’s law expo­nent show only weak depen­dency on author­ship. Also assessed was the con­tri­bu­tion from each mea­sure­ment to author­ship recog­ni­tion using three machine learn­ing meth­ods. The best per­for­mance was a ca. 65 % accu­racy upon com­bin­ing com­plex net­work and inter­mit­tency fea­tures with the near­est neigh­bor algo­rithm. From a detailed analy­sis of the inter­de­pen­dence of the var­i­ous met­rics it is con­cluded that the meth­ods used here are com­ple­men­tary for pro­vid­ing short– and long-​​scale per­spec­tives of texts, which are use­ful for appli­ca­tions such as iden­ti­fi­ca­tion of top­i­cal words and infor­ma­tion retrieval.

    natural-​​language-​​processing document-​​clustering clus­ter­ing feature-​​selection algo­rithms nudge-​​targets

  • [1108.1170] Con­vex Opti­miza­tion with­out Pro­jec­tion Steps

    For the gen­eral prob­lem of min­i­miz­ing a con­vex func­tion over a com­pact con­vex domain, we will inves­ti­gate a sim­ple iter­a­tive approx­i­ma­tion algo­rithm based on the method by Frank & Wolfe 1956, that does not need pro­jec­tion steps in order to stay inside the opti­miza­tion domain. Instead of a pro­jec­tion step, the lin­earized prob­lem defined by a cur­rent sub­gra­di­ent is solved, which gives a step direc­tion that will nat­u­rally stay in the domain. Our frame­work gen­er­al­izes the sparse greedy algo­rithm of Frank & Wolfe and its primal-​​dual analy­sis by Clark­son 2010 (and the low-​​rank SDP approach by Hazan 2008) to arbi­trary con­vex domains. We give a con­ver­gence proof guar­an­tee­ing {epsilon}-small dual­ity gap after O(1/{epsilon}) iter­a­tions. The method allows us to under­stand the spar­sity of approx­i­mate solu­tions for any l1-​​regularized con­vex opti­miza­tion prob­lem (and for opti­miza­tion over the sim­plex), expressed as a func­tion of the approx­i­ma­tion qual­ity. We obtain match­ing upper and lower bounds of {Theta}(1/{epsilon}) for the spar­sity for l1-​​problems. The same bounds apply to low-​​rank semi­def­i­nite opti­miza­tion with bounded trace, show­ing that rank O(1/{epsilon}) is best pos­si­ble here as well. As another appli­ca­tion, we obtain sparse matri­ces of O(1/{epsilon}) non-​​zero entries as {epsilon}-approximate solu­tions when opti­miz­ing any con­vex func­tion over a class of diag­o­nally dom­i­nant sym­met­ric matri­ces. We show that our pro­posed first-​​order method also applies to nuclear norm and max-​​norm matrix opti­miza­tion prob­lems. For nuclear norm reg­u­lar­ized opti­miza­tion, such as matrix com­ple­tion and low-​​rank recov­ery, we demon­strate the prac­ti­cal effi­ciency and scal­a­bil­ity of our algo­rithm for large matrix prob­lems, as e.g. the Net­flix dataset. For gen­eral con­vex opti­miza­tion over bounded matrix max-​​norm, our algo­rithm is the first with a con­ver­gence guar­an­tee, to the best of our knowledge.

    operations-​​research opti­miza­tion algo­rithms nudge-​​targets

  • [1112.6235] Detect­ing a Vec­tor Based on Lin­ear Measurements

    We con­sider a sit­u­a­tion where the state of a sys­tem is rep­re­sented by a real-​​valued vec­tor. Under nor­mal cir­cum­stances, the vec­tor is zero, while an event man­i­fests as non-​​zero entries in this vec­tor, pos­si­bly few. Our inter­est is in the design of algo­rithms that can reli­ably detect events (i.e., test whether the vec­tor is zero or not) with the least amount of infor­ma­tion. We place our­selves in a sit­u­a­tion, now com­mon in the sig­nal pro­cess­ing lit­er­a­ture, where infor­ma­tion about the vec­tor comes in the form of noisy lin­ear mea­sure­ments. We derive infor­ma­tion bounds in an active learn­ing setup and exhibit some sim­ple near-​​optimal algo­rithms. In par­tic­u­lar, our results show that the task of detec­tion within this set­ting is at once much eas­ier, sim­pler and dif­fer­ent than the tasks of esti­ma­tion and sup­port recovery.

    signal-​​processing sta­tis­tics algo­rithms nudge-​​targets

  • [1109.2215] Find­ing miss­ing edges and com­mu­ni­ties in incom­plete networks

    Many algo­rithms have been pro­posed for pre­dict­ing miss­ing edges in net­works, but they do not usu­ally take account of which edges are miss­ing. We focus on net­works which have miss­ing edges of the form that is likely to occur in real net­works, and com­pare algo­rithms that find these miss­ing edges. We also inves­ti­gate the effect of this kind of miss­ing data on com­mu­nity detec­tion algorithms.

    network-​​theory algo­rithms infer­ence sta­tis­tics nudge-​​targets

  • [1010.4735] Explor­ing the Energy Land­scapes of Pro­tein Fold­ing Sim­u­la­tions with Bayesian Computation

    Nested sam­pling is a Bayesian sam­pling tech­nique devel­oped to explore prob­a­bil­ity dis­tri­b­u­tions lo– calised in an expo­nen­tially small area of the para­me­ter space. The algo­rithm pro­vides both pos­te­rior sam­ples and an esti­mate of the evi­dence (mar­ginal like­li­hood) of the model. The nested sam­pling algo– rithm also pro­vides an effi­cient way to cal­cu­late free ener­gies and the expec­ta­tion value of ther­mo­dy­namic observ­ables at any tem­per­a­ture, through a sim­ple post-​​processing of the out­put. Pre­vi­ous appli­ca­tions of the algo­rithm have yielded large effi­ciency gains over other sam­pling tech­niques, includ­ing par­al­lel tem­per­ing (replica exchange). In this paper we describe a par­al­lel imple­men­ta­tion of the nested sam­pling algo­rithm and its appli­ca­tion to the prob­lem of pro­tein fold­ing in a Go-​​type force field of empir­i­cal poten­tials that were designed to sta­bi­lize sec­ondary struc­ture ele­ments in room-​​temperature sim­u­la­tions. We demon­strate the method by con­duct­ing fold­ing sim­u­la­tions on a num­ber of small pro­teins which are com­monly used for test­ing pro­tein fold­ing pro­ce­dures: pro­tein G, the SH3 domain of Src tyro­sine kinase and chy­motrypsin inhibitor 2. A topo­log­i­cal analy­sis of the pos­te­rior sam­ples is per­formed to pro­duce energy land­scape charts, which give a high level descrip­tion of the poten­tial energy sur­face for the pro­tein fold­ing sim­u­la­tions. These charts pro­vide qual­i­ta­tive insights into both the fold­ing process and the nature of the model and force field used.

    structural-​​biology bio­chem­istry mod­el­ing algo­rithms sta­tis­tics meta­mod­el­ing

  • [1109.2618] Fast and Accu­rate Mod­el­ing of Mol­e­c­u­lar Atom­iza­tion Ener­gies with Machine Learning

    We intro­duce a machine learn­ing model to pre­dict atom­iza­tion ener­gies of a diverse set of organic mol­e­cules, based on nuclear charges and atomic posi­tions only. The prob­lem of solv­ing the mol­e­c­u­lar Schr“odinger equa­tion is mapped onto a non-​​linear sta­tis­ti­cal regres­sion prob­lem of reduced com­plex­ity. Regres­sion mod­els are trained on and com­pared to atom­iza­tion ener­gies com­puted with hybrid density-​​functional the­ory. Cross-​​validation over more than seven thou­sand small organic mol­e­cules yields a mean absolute error of ~10 kcal/​mol. Applic­a­bil­ity is demon­strated for the pre­dic­tion of mol­e­c­u­lar atom­iza­tion poten­tial energy curves.

    machine-​​learning learning-​​from-​​data bio­chem­istry computational-​​science nudge-​​targets

  • [1101.2135] Bounded con­fi­dence model: addressed infor­ma­tion main­tain diver­sity of opinions

    A com­mu­nity of agents is sub­ject to a stream of mes­sages, which are rep­re­sented as points on a plane of issues. Mes­sages are sent by media and by agents them­selves. Mes­sages from media shape the pub­lic opin­ion. They are unbi­ased, i.e. pos­i­tive and neg­a­tive opin­ions on a given issue appear with equal fre­quen­cies. In our pre­vi­ous work, the only cri­te­rion to receive a mes­sage by an agent is if the dis­tance between this mes­sage and the ones received ear­lier does not exceed the given value of the tol­er­ance para­me­ter. Here we intro­duce a pos­si­bil­ity to address a mes­sage to a given neigh­bour. We show that this option reduces the una­nim­ity effect, what improves the col­lec­tive performance.

    agent-​​based com­mu­ni­ca­tion network-​​theory machine-​​learning diver­sity

Items of some interest…

Posted on by

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

Items of some interest…

Posted on by

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

  • A sec­ond front

    “Increas­ingly, this seems to be a war for sur­vival.  I under­stand that tra­di­tional pub­lish­ers are get­ting more and more des­per­ate as the dig­i­tal rev­o­lu­tion pro­ceeds and they con­tinue to dither about how to address it.  But aca­d­e­mic fac­ulty mem­bers are the source of almost all the con­tent these pub­lish­ers pub­lish, so this behav­ior is an extreme exam­ple of bit­ing the hand that feeds them.  It is even more stu­pid, in my opin­ion, than the strat­egy of record­ing indus­try who is suing its own cus­tomers, because these pub­lish­ers are attack­ing a group that is both their cus­tomers and those who sup­ply them with a prod­uct in the first place.”

    copy­right academic-​​culture libraries good-​​eating-​​on-​​one-​​of-​​those disintermediation-​​targets

  • jQuery for Absolute Begin­ners: The Com­plete Series | Nettuts+

    “Hi every­one! Today, I posted the final screen­cast in my “jQuery for Absolute Begin­ners” series on the The­me­For­est Blog. If you’re unfa­mil­iar – over the course of about a month, I posted fif­teen video tuto­ri­als that teach you EXACTLY how to use the jQuery library. We start by down­load­ing the library and even­tu­ally work our way up to cre­at­ing an AJAX style-​​switcher. I’m very proud of this series; pos­si­bly more than any other that I’ve done for Envato.”

    javascript jQuery tuto­r­ial pod­cast video

  • Noodle­soft: Hazel FAQ

    “In gen­eral, Hazel can mon­i­tor any folder but keep in mind that cer­tain fold­ers may not be good can­di­dates. For instance, P2P and other apps that might down­load a file slowly, may have their files moved before they are com­pletely down­loaded. In cases like this, it is best if the pro­gram has an option to down­load to one folder then move them auto­mat­i­cally to another (Trans­mis­sion has such an option). This sec­ond folder is the one you should have Hazel mon­i­tor. Hazel does have spe­cial sup­port for Safari, Camino, Fire­fox, Mail and Speed Down­load and knows how to iden­tify when their down­loads are com­plete. We will be adding sup­port for more apps as time goes on so if you have a favorite app of yours you would like sup­ported, please let us know and we’ll look into adding support.”

    util­i­ties MacOS power-​​user sysad­min

  • Kate Oneal and the Myth­i­cal Ital­ian Restau­rant | xPro​gram​ming​.com

    ‘“The artist sug­gested this: ‘Let’s set a dead­line and total bud­get. I’ll keep you posted on how much is being spent, and of course we’ll have the pic­ture on the wall to look at. By the time we’re about half-​​way through, it should be of high enough qual­ity, and have enough pic­ture ele­ments, that we could stop any time. You’ll have more ideas, of course, but by then we’ll both have a sense of how fast we can progress, and you can choose the most valu­able things to add or change. You’ll have total con­trol over how the pic­ture winds up, and if you want to, we can stop on or before the money runs out.’ “Guido wasn’t entirely con­vinced. He wanted to know how he could be sure he wouldn’t be left with a hor­ri­bly ugly wall. The artist told him that she would guar­an­tee to paint it back over and stop any time he wanted, and said she would start by work­ing in some tem­po­rary pig­ment like chalk, so they could erase and change things easily.’

    project-​​management metaphor agile-​​management

  • Our Waste­ful Health Care Sys­tem — NYTimes​.com

    “The other key thing to pay atten­tion to is who this mar­ket­ing cam­paign was tar­geted at: key deci­sion­mak­ers at providers and insur­ance com­pa­nies. Those are the peo­ple who decide whether med­ical pro­ce­dures get ordered. It’s not patients. Patients aren’t going to expe­ri­ence a loss of free­dom or sat­is­fac­tion because an expert reviewer at the Inde­pen­dant Pay­ment Advi­sory Board makes the call as to whether a pro­ce­dure is med­ically ben­e­fi­cial, rather than the cor­re­spond­ing bureau­crat at their insur­ance provider or at the for-​​profit clinic they’re attending.”

    medical-​​culture cor­po­ratism public-​​policy insur­ance health­care mar­ket­ing

  • The Value of Fol­low­ing Pas­sion in a Job­less World — Lane Wal­lace — Life — The Atlantic

    “If I were a 22-​​year-​​old read­ing all this, the whole notion of adult­hood would seem like a prison sen­tence worth try­ing to avoid. But more impor­tantly, the entire premise upon which all this advice is based is false.  Pas­sion, despite how often we use the term to tout com­pany com­mit­ment or extol roman­tic excite­ment, is often mis­un­der­stood or con­fused with other motivations. Many peo­ple view dreams and pas­sion exactly as Brooks painted it: as a hope­lessly ide­al­is­tic, self­ish, or irre­spon­si­ble choice that is dia­met­ri­cally opposed to com­mit­ment to oth­ers, respon­si­bil­ity, secu­rity, or success. But I have spent the past year and a half research­ing a book about pas­sion and peo­ple who fol­low pas­sion­ate paths in life, and noth­ing I’ve found backs up that premise or belief. Indeed, I would argue that pas­sion is one of the most impor­tant ele­ments in any effort to improve a com­mu­nity, build some­thing of value in the world, and even sur­vive tough times or a daunt­ing econ­omy. The fact that it also tends to lead to a sense of ful­fill­ment within an indi­vid­ual is cer­tainly one of its benefits—but it’s not the dri­ving force that com­pels some­one down the pas­sion road.”

    work­life moti­va­tion David-Brooks-doesn’t-deserve-a-lot-of-respect pas­sion

  • Let It Roll — CFO Mag­a­zine — May 2011 Issue — CFO​.com

    “Sep­a­rat­ing the three deci­sions has enabled the com­pany to set tar­gets that are more ambi­tious, intel­li­gent, and moti­vat­ing, says Bogsnes. As a result, the fore­casts are less biased, and resource allo­ca­tion is more dynamic and self-​​regulating. “The ‘bank’ is open 12 months a year, not just six weeks in the fall,” he says. “By mak­ing resource deci­sions as late as pos­si­ble instead of in an annual bud­get, we have bet­ter infor­ma­tion — not just about project attrac­tive­ness but also about our capac­ity to fund or man new projects.“ Encour­aged by pos­i­tive results from aban­don­ing the bud­get, Sta­toil recently decided to abol­ish the cal­en­dar year as a plan­ning tool and intro­duce a busi­ness– and event-​​driven man­age­ment process in its stead.”

    bud­get­ing finance man­age­ment plan­ning fore­cast­ing agility

  • About That Recipe

    “Inter­preters are cou­plers. They enable the two peo­ple, groups, or cul­tures to under­stand each other because they under­stand both. While the meth­ods men­tioned above can facil­i­tate a fur­ther under­stand­ing of past food cul­tures, what about the other part of the connection—between peo­ple today and in the future? The his­tor­i­cal inter­preter has the unusual task of cou­pling peo­ple in one group about which she can only know a part, one group she knows well, and, if she pub­lishes her inter­pre­ta­tion in any form, one group in the future, about which she can­not know. The ques­tion is, then, not only what can we learn about mean­ings in the past, but how can we inter­pret those mean­ings to peo­ple today and in the future?”

    quotable his­tory

  • Lan­guage Log » Straw men and Bee Science

    “Let me start by say­ing that there’s a way to take all this that makes it entirely cor­rect. The key motive of sci­ence is expla­na­tion, and it’s often essen­tial to abstract away from the com­plex­i­ties of raw obser­va­tion, and so on. I took courses from Chom­sky as an under­grad­u­ate and a grad­u­ate stu­dent, and I’m grate­ful for what I learned from him, and for the emi­nently fair way that he always treated me. But increas­ingly, it seems to me, he has been ele­vat­ing his per­sonal dis­taste for the com­plex­i­ties of the real world into a sys­tem­atic phi­los­o­phy. To the extent that oth­ers accept these views, it excludes them from par­tic­i­pa­tion in (what I think are) the most promis­ing and excit­ing cur­rent direc­tions in the sci­ences of speech and language.”

    Noam-​​Chomsky theory-​​and-​​practice-​​sitting-​​in-​​a-​​tree bias sci­ence learning-​​from-​​data

  • Bozo Sapi­ens: Robert Owen: Laboriousness

    “Owen had neglected to notice that expec­ta­tions also change through cir­cum­stance. As our com­mu­nal con­di­tions advance, we all tend to want to become the prophet, not merely the con­gre­ga­tion. Once the prob­lem of sur­vival is solved, it’s no longer enough not to be starv­ing or abused or over­worked – we want per­sonal sat­is­fac­tion and self-​​direction. So, yes: some of the great names in busi­ness – the Low­ell mills, Hershey’s, Cadbury’s, Lever Broth­ers, Google – applied dilute Owenism to great effect, but suc­cess makes employ­ees become more indi­vid­u­al­ist and ask for more of their reward in cash, while hard times make share­hold­ers less gen­er­ous, point­ing out that plenty of peo­ple would take the job with­out the crêche, lec­ture series, or com­pany brass band. Shift­ing expec­ta­tion dri­ves the carousel for another turn; we remain ambiva­lent about work, this thing we do through most of our wak­ing lives, because we still don’t know what it is for.”

    institutional-​​design col­lab­o­ra­tion workantile-​​exchange diver­sity plan-​​for-​​change

  • Cal­cu­lated Risk: The Excess Vacant Hous­ing Supply

    “It is no sur­prise that Florida has the largest num­ber of excess vacant units and that Nevada has the largest per­cent­age of excess vacant units. What might be a sur­prise to some is that Cal­i­for­nia is below the U.S. average.”

    financial-​​crisis real-​​estate housing-​​bubble public-​​policy

  • Strin­gent Response: Sys­tems biol­ogy approach to strin­gent response

    “All this results in bac­te­ria gam­bling all the time: some react to stim­u­lus, some don’t, some pro­duce more pro­teins in response to it, some less. This leads to so called phe­no­typic het­ero­gene­ity, when oth­er­wise (genet­i­cally) iden­ti­cal bac­te­ria become very dif­fer­ent in terms of their responses. This could be a good thing and also could be a bad thing. Hav­ing a col­lec­tion of dif­fer­ent bugs instead of a clone army will pro­vide cer­tain ver­sa­til­ity: some are ready for one con­di­tions, and some are ready for oth­ers. For instance, some are ready to grow and divide right away and some are slower and more cau­tious. Both types of cells can be ben­e­fi­cial in dif­fer­ent con­di­tions: the active ones will drive the pop­u­la­tion growth, but will be sen­si­tive to the antibi­otic treat­ment, and the pas­sive ones will wait until the treat­ment is over and then they will come to life. Sounds like a good strat­egy (and it has a name, this strat­egy — “bed hedg­ing”) and I guess it is exactly the rea­son why clone armies never caught on.”

    diver­sity systems-​​biology evolutionary-​​biology game-​​theory emergent-​​design

  • Time as a Com­pet­i­tive Advan­tage | Mike Cohn’s Blog — Suc­ceed­ing With Agile®

    “Inno­va­tion has become a fer­tile area in which com­pa­nies seek com­pet­i­tive advan­tage today. This has served Apple well over the past decade. I don’t think inno­v­a­tive­ness will be going away soon as a source of com­pet­i­tive advan­tage. But I do won­der whether time is run­ning out on time as a com­pet­i­tive advan­tage. If agile and other inno­va­tions lead us to a world where all com­pa­nies can deliver new prod­ucts and ser­vices equally quickly, com­pa­nies will need to find newer ways to dif­fer­en­ti­ate themselves.”

    inno­va­tion com­pet­i­tive­ness agility strat­egy

  • See­ing Things On Mars: A Long His­tory of Mar­t­ian Illu­sions and Human Delu­sions |Parei­do­lia & Opti­cal Illu­sions | Space​.com

    “Humans have been see­ing strange things on the sur­face of Mars for cen­turies. From the 1700s up through the present day, wide­spread fame has been avail­able to any­one able to pro­duce even the slight­est bit of flimsy evi­dence that there’s Mar­t­ian life.”

    nanohis­tory Mars psy­choce­ram­ics astron­omy belief optical-​​illusions

  • The rise of Glen­core, the biggest com­pany you’ve never heard of | Busi­ness | The Guardian

    “But so jeal­ously has Glasen­berg guarded his pri­vacy that his name means noth­ing to the man on the street. For years he has avoided speeches and, until recently, had given only one inter­view – to his old uni­ver­sity mag­a­zine. If you live out­side the world of com­modi­ties trad­ing or cor­po­rate finance, Ivan Glasen­berg is prob­a­bly the Most Impor­tant Busi­ness­man You Have Never Heard Of.”

    glob­al­iza­tion finance cor­po­ra­tions pri­vacy transparency-it-ain’t

  • Datameer snags $9.25M more to ana­lyze mas­sive amounts of data | VentureBeat

    “Datameer, a com­pany that allows users to ana­lyze mas­sive amounts of data with­out tech­ni­cal know-​​how, today announced a sec­ond round of fund­ing for $9.25 mil­lion. The money will be used to hire addi­tional employ­ees for its engi­neer­ing, sales, and mar­ket­ing teams.”

    data-​​analysis data-​​mining star­tups fund­ing bub­b­li­cious

  • Plan Would Force U. of Wis­con­sin to Return $39-​​Million in U.S. Broad­band Grants — Wired Cam­pus — The Chron­i­cle of Higher Education

    “Another pro­vi­sion in the plan would bar any Uni­ver­sity of Wis­con­sin cam­pus from par­tic­i­pat­ing in advanced net­works con­nect­ing research insti­tu­tions world­wide, accord­ing to Mr. Evers’s memo. For exam­ple, the Madi­son cam­pus would have to with­draw from Internet2, a high-​​speed net­work­ing con­sor­tium, said Mr. Giroux.”

    pol­i­tics Wis­con­sin stu­pid­ity broad­band telecom­mu­ni­ca­tions cor­po­ratism