Items of some interest:

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

  • Cin­derella Doc­u­men­ta­tion : The­o­ret­i­cal Background

    “At first sight it is not clear whether this require­ment is sat­is­fi­able in gen­eral. Turn on your favorite sys­tem for doing inter­ac­tive geom­e­try or para­met­ric CAD and make the fol­low­ing exper­i­ment: Draw a hor­i­zon­tal line and con­struct two cir­cles of equal radius whose cen­ters are con­strained to slide along the line. Move the cir­cles to a posi­tion in which they inter­sect, and con­struct the upper point of inter­sec­tion of the two cir­cles. Now move one cir­cle so that its cen­ter passes through the cen­ter of the other cir­cle. Most prob­a­bly you will see that the point of inter­sec­tion sud­denly jumps from the upper inter­sec­tion to the lower one. This is what has hap­pened in all the sys­tems we have tried so far. Such behav­ior runs counter to our require­ment of con­ti­nu­ity: You make a small move, and a depen­dent point sud­denly jumps.”

    sim­u­la­tion geom­e­try algo­rithms rep­re­sen­ta­tion nudge-​​targets
  • Reli­gion and the City | New​geog​ra​phy​.com

    “He talks about items rang­ing from mul­ti­cul­tural sen­si­tiv­i­ties to tak­ing the arts seri­ous to “being famous for help­ing the poor.” The lat­ter was an item that jumped out at me because, as I’ve noted before, too many urban­ist argu­ments are basi­cally argu­ments for what I call “Star­bucks urban­ism.” If called on this, peo­ple will say, “But of course tran­sit will ben­e­fit the poor too.” But that’s not how it’s sold. Urban­ists ought to be famous for the way they design, imple­ment, and talk about their poli­cies as instru­ments for help­ing the poor and facil­i­tat­ing upward eco­nomic and social mobil­ity. There’s a lot of other good stuff in the video that’s rel­e­vant to urban­ism. For those who pre­fer read­ing, Keller also wrote a paper called “Our New Global Cul­ture: Min­istry in Cities, which says of itself: “This paper sur­veys the rise of global cities, the cul­ture and dom­i­nant world­views within these cities, and a frame­work for min­is­ter­ing in them.”?

    city-​​planning orga­ni­za­tion mar­ket­ing Workan­tile Coscience out­reach diver­sity man­age­ment
  • Study Hacks » Blog Archive » What You Know Mat­ters More Than What You Do

    “Accord­ing to my col­leagues, this star researcher tends to begin with tech­niques, not prob­lems. He first mas­ters a tech­nique that seems promis­ing (and when I say “mas­ter,” I mean it — he really goes deep in build­ing his under­stand­ing). He then uses this new tech­nique to seek out prob­lems that were once hard but now yield eas­ily. He’s rest­less in this quest, often mas­ter­ing sev­eral new tech­niques each year.”

    heuris­tics work­life inno­va­tion pro­duc­tiv­ity problem-​​seeing problem-​​solving
  • Jour­natic worker takes ‘This Amer­i­can Life’ inside out­sourced jour­nal­ism | Poynter.

    “If you’ve never heard of Jour­natic, that’s kind of the idea. The com­pany, which was founded in 2006, has a web­site that doesn’t appear on at least the first five pages of Google search results. Job open­ings, often posted on Craigslist or Jour​nal​is​mJobs​.com, once men­tioned the company’s name, but no longer. Jour­natic cur­rently works with “dozens” of media com­pa­nies, Tim­pone said, though he declined to name them. He’s spo­ken before of the real estate sec­tion Jour­natic pro­duces for the San Fran­cisco Chron­i­cle. He said more are sign­ing up all the time.”

    disintermediation-​​in-​​action jour­nal­ism work­life out­sourc­ing exposé
  • A Step-​​by-​​Step Guide to Tribal Lead­er­ship: Part 1: The Five Stages of Tribal Cul­ture « emer­gent by design

    “Tribal Lead­ers are the peo­ple who focus their efforts on upgrad­ing the tribal cul­ture. (upgrad­ing the words we use to describe our real­ity and the behav­iors we prac­tice that shape the direc­tion of our lives) They set the stan­dard of per­for­mance in their indus­tries, from pro­duc­tiv­ity and prof­itabil­ity to employee reten­tion, and attract tal­ent. Most of all, they help bring groups to unity by rec­og­niz­ing their ‘trib­al­ness’ – get­ting peo­ple to talk about the things they really care about, com­ing together around these com­mon causes, and form­ing mis­sions to make some­thing great hap­pen, and to live in great­ness. The goal of Tribal Lead­er­ship is to learn how to get peo­ple ‘unstuck’ – from unhelp­ful lan­guage and behav­iors, so we can level up and tran­si­tion into higher-​​performance, less stress­ful, and more fun states of Being.”

    i-​​hate-​​the-​​word-​​tribes col­lab­o­ra­tion lead­er­ship cultural-​​dynamics advice
  • [1206.6532] Esti­mat­ing Nui­sance Para­me­ters in Inverse Problems

    “Many inverse prob­lems include nui­sance para­me­ters which, while not of direct inter­est, are required to recover pri­mary para­me­ters. Struc­ture present in these prob­lems allows effi­cient opti­miza­tion strate­gies — a well known exam­ple is vari­able pro­jec­tion, where non­lin­ear least squares prob­lems which are lin­ear in some para­me­ters can be very effi­ciently opti­mized. In this paper, we extend the idea of pro­ject­ing out a sub­set over the vari­ables to a broad class of max­i­mum like­li­hood (ML) and max­i­mum a pos­te­ri­ori like­li­hood (MAP) prob­lems with nui­sance para­me­ters, such as vari­ance or degrees of free­dom. As a result, we are able to incor­po­rate nui­sance para­me­ter esti­ma­tion into large-​​scale con­strained and uncon­strained inverse prob­lem for­mu­la­tions. We apply the approach to a vari­ety of prob­lems, includ­ing esti­ma­tion of unknown vari­ance para­me­ters in the Gauss­ian model, degree of free­dom (d.o.f.) para­me­ter esti­ma­tion in the con­text of robust inverse prob­lems, auto­matic cal­i­bra­tion, and opti­mal exper­i­men­tal design. Using numer­i­cal exam­ples, we demon­strate improve­ment in recov­ery of pri­mary para­me­ters for sev­eral large– scale inverse prob­lems. The pro­posed approach is com­pat­i­ble with a wide vari­ety of algo­rithms and for­mu­la­tions, and its imple­men­ta­tion requires only minor mod­i­fi­ca­tions to exist­ing algorithms.”

    reinventing-​​the-​​wheel feature-​​extraction opti­miza­tion modeling-​​is-​​not-​​mathematics nudge-​​targets
  • [1206.4608] A Hybrid Algo­rithm for Con­vex Semi­def­i­nite Optimization

    “We present a hybrid algo­rithm for opti­miz­ing a con­vex, smooth func­tion over the cone of pos­i­tive semi­def­i­nite matri­ces. Our algo­rithm con­verges to the global opti­mal solu­tion and can be used to solve gen­eral large-​​scale semi­def­i­nite pro­grams and hence can be read­ily applied to a vari­ety of machine learn­ing prob­lems. We show exper­i­men­tal results on three machine learn­ing prob­lems (matrix com­ple­tion, met­ric learn­ing, and sparse PCA) . Our approach out­per­forms state-​​of-​​the-​​art algorithms.”

    algo­rithms opti­miza­tion computational-​​complexity spe­cial­iza­tion nudge-​​targets
  • [1206.6690] Gen­er­a­tion and Prop­er­ties of Snarks

    “For many of the unsolved prob­lems con­cern­ing cycles and match­ings in graphs it is known that it is suf­fi­cient to prove them for emph{snarks}, the class of non­triv­ial 3-​​regular graphs which can­not be 3-​​edge coloured. In the first part of this paper we present a new algo­rithm for gen­er­at­ing all non-​​isomorphic snarks of a given order. Our imple­men­ta­tion of the new algo­rithm is 14 times faster than pre­vi­ous pro­grams for gen­er­at­ing snarks, and 29 times faster for gen­er­at­ing weak snarks. Using this pro­gram we have gen­er­ated all non-​​isomorphic snarks on $nleq 36$ ver­tices. Pre­vi­ously lists up to $n=28$ ver­tices have been pub­lished. In the sec­ond part of the paper we ana­lyze the sets of gen­er­ated snarks with respect to a num­ber of prop­er­ties and con­jec­tures. We find that some of the strongest ver­sions of the cycle dou­ble cover con­jec­ture hold for all snarks of these orders, as does Jaeger’s Petersen colour­ing con­jec­ture, which in turn implies that Fulkerson’s con­jec­ture has no small coun­terex­am­ples. In con­trast to these pos­i­tive results we also find coun­terex­am­ples to eight pre­vi­ously pub­lished con­jec­tures con­cern­ing cycle cov­er­ings and the gen­eral cycle struc­ture of cubic graphs.”

    graph-​​theory com­bi­na­torics algo­rithms nudge-​​targets
  • [1206.6238] Entrain­abil­ity enhance­ment by period mis­match in biloop genetic oscillators

    “Effects of the period mis­match on entrain­ment prop­er­ties in two cou­pled genetic oscil­la­tors are stud­ied. The entrain­ment is cal­cu­lated with a phase reduc­tion approach and a Flo­quet mul­ti­plier analy­sis, and their depen­den­cies on cou­pling strength and the period ratio are inves­ti­gated in two genetic oscil­la­tor mod­els (smooth and relax­ation oscil­la­tors). We find that the exis­tence of the period mis­match induces an enhance­ment of entrain­ment in both smooth and relax­ation oscil­la­tors. By cal­cu­lat­ing Flo­quet mul­ti­pli­ers, we show that the enhance­ment mech­a­nism is based on the cou­pled oscil­la­tors which are in the vicin­ity of bifur­ca­tion on limit cycle.”

    biological-​​engineering emergent-​​design reaction-​​networks oscil­la­tors control-​​theory
  • [1206.4672] Effi­cient Active Algo­rithms for Hier­ar­chi­cal Clustering

    “Advances in sens­ing tech­nolo­gies and the growth of the inter­net have resulted in an explo­sion in the size of mod­ern datasets, while stor­age and pro­cess­ing power con­tinue to lag behind. This moti­vates the need for algo­rithms that are effi­cient, both in terms of the num­ber of mea­sure­ments needed and run­ning time. To com­bat the chal­lenges asso­ci­ated with large datasets, we pro­pose a gen­eral frame­work for active hier­ar­chi­cal clus­ter­ing that repeat­edly runs an off-​​the-​​shelf clus­ter­ing algo­rithm on small sub­sets of the data and comes with guar­an­tees on per­for­mance, mea­sure­ment com­plex­ity and run­time com­plex­ity. We instan­ti­ate this frame­work with a sim­ple spec­tral clus­ter­ing algo­rithm and pro­vide con­crete results on its per­for­mance, show­ing that, under some assump­tions, this algo­rithm recov­ers all clus­ters of size ?(log n) using O(n log^2 n) sim­i­lar­i­ties and runs in O(n log^3 n) time for a dataset of n objects. Through exten­sive exper­i­men­ta­tion we also demon­strate that this frame­work is prac­ti­cally alluring.”

    clus­ter­ing algo­rithms nudge-​​targets practically-​​alluring
  • Most Bla­tant Pro-​​ACTA Cam­paign So Far Is A Copy­right Monop­oly Vio­la­tion — Falkvinge on Infopolicy

    “This episode shows clearer than ever that the copy­right and patent monop­o­lies are not intended to be pro­tec­tive of inno­va­tion or pro­tec­tive of the econ­omy. They’re obvi­ously too com­plex even for their strongest sup­port­ers and lob­by­ists to under­stand and adhere to. Rather, they are intended as legal clubs to be used by the now-​​rich incum­bents against resource-​​strapped upstarts. The copy­right and patent monop­o­lies are only pro­tec­tive of the past, pro­tec­tive against the present and future of inno­va­tion, cre­ativ­ity, and economy.”

    copy­right intellectual-​​property cor­po­ratism public-​​policy
  • [1112.5218] Pat­terns of neu­tral diver­sity under gen­eral mod­els of selec­tive sweeps

    “Two major sources of sto­chas­tic­ity in the dynam­ics of neu­tral alle­les result from resam­pling of finite pop­u­la­tions (genetic drift) and the ran­dom genetic back­ground of nearby selected alle­les on which the neu­tral alle­les are found (linked selec­tion). There is now good evi­dence that linked selec­tion plays an impor­tant role in shap­ing poly­mor­phism lev­els in a num­ber of species. One of the best inves­ti­gated mod­els of linked selec­tion is the recur­rent full sweep model, in which newly arisen selected alle­les fix rapidly. How­ever, the bulk of selected alle­les that sweep into the pop­u­la­tion may not be des­tined for rapid fix­a­tion. Here we develop a gen­eral model of recur­rent selec­tive sweeps in a coa­les­cent frame­work, one that gen­er­al­izes the recur­rent full sweep model to the case where selected alle­les do not sweep to fix­a­tion. We show that in a large pop­u­la­tion, only the ini­tial rapid increase of a selected allele affects the geneal­ogy at par­tially linked sites, which under fairly gen­eral assump­tions are unaf­fected by the sub­se­quent fate of the selected allele. We also apply the the­ory to a sim­ple model to inves­ti­gate the impact of recur­rent par­tial sweeps on lev­els of neu­tral diver­sity, and find that for a given reduc­tion in diver­sity, the impact of recur­rent par­tial sweeps on the fre­quency spec­trum at neu­tral sites is deter­mined pri­mar­ily by the fre­quen­cies achieved by the selected alle­les. Con­se­quently, recur­rent sweeps of selected alle­les to low fre­quen­cies can have a pro­found effect on lev­els of diver­sity but can leave the fre­quency spec­trum rel­a­tively unper­turbed. In fact, the lim­it­ing coa­les­cent model under a high rate of sweeps to low fre­quency is iden­ti­cal to the stan­dard neu­tral model. The gen­eral model of selec­tive sweeps we describe goes some way towards pro­vid­ing a more flex­i­ble frame­work to describe genomic pat­terns of diver­sity than is cur­rently available.”

    neutral-​​networks evolutionary-​​dynamics fitness-​​landscapes diver­sity theoretical-​​biology
  • [1206.3520] Recov­er­ing the tree-​​like trend of evo­lu­tion despite exten­sive lat­eral genetic trans­fer: A prob­a­bilis­tic analysis

    “In the pres­ence of high­ways, deal­ing with more gen­eral net­work set­tings would be desir­able. Also our def­i­n­i­tion of high­ways as con­nect­ing two edges is some­what restric­tive. In gen­eral, one is also inter­ested in pref­er­en­tial genetic trans­fers between clades.”

    algo­rithms lateral-​​gene-​​transfer cladis­tics phy­lo­ge­net­ics inverse-​​problems ontol­ogy modeling-​​is-​​not-​​mathematics nudge-​​targets
  • [1206.3279] The Phy­lo­ge­netic Indian Buf­fet Process: A Non-​​Exchangeable Non­para­met­ric Prior for Latent Features

    “Non­para­met­ric Bayesian mod­els are often based on the assump­tion that the objects being mod­eled are exchange­able. While appro­pri­ate in some appli­ca­tions (e.g., bag-​​of-​​words mod­els for doc­u­ments), exchange­abil­ity is some­times assumed sim­ply for com­pu­ta­tional rea­sons; non-​​exchangeable mod­els might be a bet­ter choice for appli­ca­tions based on sub­ject mat­ter. Draw­ing on ideas from graph­i­cal mod­els and phy­lo­ge­net­ics, we describe a non-​​exchangeable prior for a class of non­para­met­ric latent fea­ture mod­els that is nearly as effi­cient com­pu­ta­tion­ally as its exchange­able coun­ter­part. Our model is applic­a­ble to the gen­eral set­ting in which the depen­den­cies between objects can be expressed using a tree, where edge lengths indi­cate the strength of rela­tion­ships. We demon­strate an appli­ca­tion to mod­el­ing prob­a­bilis­tic choice.”

    sta­tis­tics algo­rithms ontol­ogy col­li­ga­tion feature-​​extraction philosophy-​​of-​​science nudge-​​targets
  • “The Eurozone’s Strat­egy is a Dis­as­ter” « naked capitalism

    “Why should Ger­man and other tax­pay­ers, mostly from the north, pay for the oth­ers, mostly from the south? Because their gov­ern­ments are respon­si­ble for the dis­as­trous sit­u­a­tion we are in.”

    financial-​​crisis public-​​policy eco­nom­ics cultural-​​dynamics fair-​​weather-​​bosses
  • [1206.6504] An Abstract Approach to Strat­i­fi­ca­tion in Lin­ear Logic

    “We study the notion of strat­i­fi­ca­tion, as used in sub­sys­tems of lin­ear logic with low com­plex­ity bounds on the cut-​​elimination pro­ce­dure (the so-​​called light log­ics), from an abstract point of view, intro­duc­ing a log­i­cal sys­tem in which strat­i­fi­ca­tion is han­dled by a sep­a­rate modal­ity. This modal­ity, which is a gen­er­al­iza­tion of the para­graph modal­ity of Girard’s light lin­ear logic, arises from a gen­eral cat­e­gor­i­cal con­struc­tion applic­a­ble to all mod­els of lin­ear logic. We thus learn that strat­i­fi­ca­tion may be for­mu­lated inde­pen­dently of expo­nen­tial modal­i­ties; when it is forced to be con­nected to expo­nen­tial modal­i­ties, it yields inter­est­ing com­plex­ity prop­er­ties. In par­tic­u­lar, from our analy­sis stem three alter­na­tive refor­mu­la­tions of Bail­lot and Mazza’s lin­ear logic by lev­els: one geo­met­ric, one inter­ac­tive, and one semantic.”

    linear-​​logic logic-​​programming for­mal­iza­tion nudge-​​targets rep­re­sen­ta­tion
  • [1205.0802] Win-​​stay-​​lose-​​learn pro­motes coop­er­a­tion in the spa­tial prisoner’s dilemma game

    “Hold­ing on to one’s strat­egy is nat­ural and com­mon if the later war­rants suc­cess and sat­is­fac­tion. This goes against wide­spread sim­u­la­tion prac­tices of evo­lu­tion­ary games, where play­ers fre­quently con­sider chang­ing their strat­egy even though their pay­offs may be mar­gin­ally dif­fer­ent than those of the other play­ers. Inspired by this obser­va­tion, we intro­duce an aspiration-​​based win-​​stay-​​lose-​​learn strat­egy updat­ing rule into the spa­tial prisoner’s dilemma game. The rule is sim­ple and intu­itive, fore­see­ing strat­egy changes only by dis­sat­is­fied play­ers, who then attempt to adopt the strat­egy of one of their near­est neigh­bors, while the strate­gies of sat­is­fied play­ers are not sub­ject to change. We find that the pro­posed win-​​stay-​​lose-​​learn rule pro­motes the evo­lu­tion of coop­er­a­tion, and it does so very robustly and inde­pen­dently of the ini­tial con­di­tions. In fact, we show that even a minute ini­tial frac­tion of coop­er­a­tors may be suf­fi­cient to even­tu­ally secure a highly coop­er­a­tive final state. In addi­tion to exten­sive sim­u­la­tion results that sup­port our con­clu­sions, we also present results obtained by means of the pair approx­i­ma­tion of the stud­ied game. Our find­ings con­tinue the suc­cess story of related win-​​stay strat­egy updat­ing rules, and by doing so reveal new ways of resolv­ing the prisoner’s dilemma.”

    game-​​theory agent-​​based com­plex­ol­ogy
  • The Rude Pundit

    “And there’s every­thing you need to know about the Repub­li­can Party. “Shit hap­pened, but so what? Peo­ple were vic­tim­ized, but why should we care? That was nearly forty years ago.” The demen­tia in refus­ing to look back­ward, refus­ing to make up for the mis­takes of the past, whether it’s the Bush tax cuts or the lies that got us into war or the lies that got us into this finan­cial cri­sis, makes us damned to repeat. ”

    sum­mary pol­i­tics Repub­li­cans

Items of some interest:

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

  • Tar­get Expres­sion Exam­ples — Eureqa Formulize

    ‘The “Tar­get Expres­sion” in the field at the top of the Set Tar­get tab tells For­mulize what type of model to search for. By default, the tar­get expres­sion is an equa­tion where y (or, if there’s no y, what­ever vari­able is in col­umn A) is mod­eled as a func­tion of all other vari­ables. To edit the tar­get expres­sion, click on it, then make the desired alter­ations. Use the spe­cial func­tion f(…) to spec­ify the part of the equa­tion that For­mulize will attempt to fill in; For­mulize will search for the for­mula f(…) using the vari­ables you put inside the parentheses.’

    for­mulize eureqa genetic-​​programming symbolic-​​regression mod­el­ing doc­u­men­ta­tion
  • A New Solu­tion to the Puz­zle of Sim­plic­ity — PhilSci-​​Archive

    “Explain­ing the con­nec­tion, if any, between sim­plic­ity and truth is among the deep­est prob­lems fac­ing the phi­los­o­phy of sci­ence, sta­tis­tics, and machine learn­ing. Say that an effi­cient truth-​​finding method min­i­mizes worst-​​case costs en route to con­verg­ing to the true answer to a the­ory choice prob­lem. Let the costs con­sid­ered include the num­ber of times a false answer is selected, the num­ber of times opin­ion is reversed, and the times at which the rever­sals occur. It is demon­strated that (1)always choos­ing the sim­plest the­ory com­pat­i­ble with expe­ri­ence and (2) hang­ing onto it while it remains sim­plest is both nec­es­sary and suf­fi­cient for efficiency.”

    via:cshalizi occam’s-razor sim­plic­ity model-​​discovery expla­na­tion philosophy-​​of-​​science
  • [1206.4599] A Uni­fied Robust Clas­si­fi­ca­tion Model

    “A wide vari­ety of machine learn­ing algo­rithms such as sup­port vec­tor machine (SVM), min­i­max prob­a­bil­ity machine (MPM), and Fisher dis­crim­i­nant analy­sis (FDA), exist for binary clas­si­fi­ca­tion. The pur­pose of this paper is to pro­vide a uni­fied clas­si­fi­ca­tion model that includes the above mod­els through a robust opti­miza­tion approach. This uni­fied model has sev­eral ben­e­fits. One is that the exten­sions and improve­ments intended for SVM become applic­a­ble to MPM and FDA, and vice versa. Another ben­e­fit is to pro­vide the­o­ret­i­cal results to above learn­ing meth­ods at once by deal­ing with the uni­fied model. We give a sta­tis­ti­cal inter­pre­ta­tion of the uni­fied clas­si­fi­ca­tion model and pro­pose a non-​​convex opti­miza­tion algo­rithm that can be applied to non-​​convex vari­ants of exist­ing learn­ing methods.”

    clas­si­fi­ca­tion algo­rithms lumpers-​​and-​​spliters-​​sittin-​​in-​​a-​​tree
  • CUDA Down­loads | NVIDIA Devel­oper Zone

    This release of the CUDA Toolkit  enables devel­op­ment using GPUs using the Kepler archi­tec­ture, such as the GeForce GTX680. Fea­ture and func­tion­al­ity builds on the foun­da­tion of the CUDA 4.1 release which intro­duced: A new  LLVM-​​based CUDA com­piler 1000+ new image pro­cess­ing func­tions Redesigned Visual Pro­filer with auto­mated per­for­mance analy­sis and inte­grated expert guidance

    CUDA GPU pro­gram­ming library MacOS
  • [1206.2057] Fin­ish­ing Flows Quickly with Pre­emp­tive Scheduling

    “Today’s data cen­ters face extreme chal­lenges in pro­vid­ing low latency. How­ever, fair shar­ing, a prin­ci­ple com­monly adopted in cur­rent con­ges­tion con­trol pro­to­cols, is far from opti­mal for sat­is­fy­ing latency require­ments. We pro­pose Pre­emp­tive Dis­trib­uted Quick (PDQ) flow sched­ul­ing, a pro­to­col designed to com­plete flows quickly and meet flow dead­lines. PDQ enables flow pre­emp­tion to approx­i­mate a range of sched­ul­ing dis­ci­plines. For exam­ple, PDQ can emu­late a short­est job first algo­rithm to give pri­or­ity to the short flows by paus­ing the con­tend­ing flows. PDQ bor­rows ideas from cen­tral­ized sched­ul­ing dis­ci­plines and imple­ments them in a fully dis­trib­uted man­ner, mak­ing it scal­able to today’s data cen­ters. Fur­ther, we develop a mul­ti­path ver­sion of PDQ to exploit path diver­sity. Through exten­sive packet-​​level and flow-​​level sim­u­la­tion, we demon­strate that PDQ sig­nif­i­cantly out­per­forms TCP, RCP and D3 in data cen­ter envi­ron­ments. We fur­ther show that PDQ is sta­ble, resilient to packet loss, and pre­serves nearly all its per­for­mance gains even given inac­cu­rate flow information.”

    queuing-​​models engineering-​​design algo­rithms performance-​​measure nudge-​​targets
  • [1206.2216] Com­plex Sys­tems Sci­ence: Dreams of Uni­ver­sal­ity, Real­ity of Interdisciplinarity

    “Using a large data­base (~ 215 000 records) of rel­e­vant arti­cles, we empir­i­cally study the “com­plex sys­tems” field and its claims to find uni­ver­sal prin­ci­ples apply­ing to sys­tems in gen­eral. The study of ref­er­ences shared by the papers allows us to obtain a global point of view on the struc­ture of this highly inter­dis­ci­pli­nary field. We show that its over­all coher­ence does not arise from a uni­ver­sal the­ory but instead from com­pu­ta­tional tech­niques and fruit­ful adap­ta­tions of the idea of self-​​organization to spe­cific sys­tems. We also find that com­mu­ni­ca­tion between dif­fer­ent dis­ci­plines goes through spe­cific “trad­ing zones”, ie sub-​​communities that cre­ate an inter­face around spe­cific tools (a DNA microchip) or con­cepts (a network).”

    via:cshalizi com­plex­ol­ogy pro­fes­sion­al­iza­tion network-​​theory disappointed-​​by-​​lack-​​of-​​Abbott-​​ref citation-​​networks

Items of some interest:

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:

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

  • Nicholas Rombes: Punk | berfrois

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

    punk history-​​is-​​a-​​feature-​​not-​​a-​​bug cultural-​​dynamics ha-​​ha-​​only-​​semiserious
  • [1112.5309] POWERPLAY: Train­ing an Increas­ingly Gen­eral Prob­lem Solver by Con­tin­u­ally Search­ing for the Sim­plest Still Unsolv­able Problem

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

    now-​​this-​​really-​​pisses-​​me-​​off-​​to-​​no-​​end
  • [1203.0856] Online Dis­crim­i­na­tive Dic­tio­nary Learn­ing for Image Clas­si­fi­ca­tion Based on Block-​​Coordinate Descent Method

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

    image-​​analysis image-​​segmentation algo­rithms nudge-​​targets
  • [1203.3271] The ther­mo­dy­nam­ics of prediction

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

    mod­el­ing philosophy-​​of-​​science information-​​theory physics ther­mo­dy­nam­ics talking-​​about-​​a-​​model-​​is-​​a-​​model pragmatism-it-ain’t
  • [1203.3434] On the Impact of Infor­ma­tion Tech­nolo­gies on Soci­ety: an His­tor­i­cal Per­spec­tive through the Game of Chess

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

    touch­stones his­tory algo­rithms history-​​of-​​science computer-​​science
  • Share Books | berfrois

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

    Occupy libraries intellectual-​​property open-​​access public-​​policy activism
  • [1112.3307] Poly­tope Codes Against Adver­saries in Networks

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

    cryp­tog­ra­phy pri­vacy algo­rithms nudge-​​targets network-​​theory com­mu­ni­ca­tion
  • [1203.3353] Solv­ing Struc­ture with Sparse, Randomly-​​Oriented X-​​ray Data

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

    structural-​​biology image-​​analysis crys­tal­log­ra­phy algo­rithms inverse-​​problems nudge-​​targets sta­tis­tics
  • [1203.3203] An effi­cient algo­rithm for gen­er­at­ing AoA networks

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

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

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

    control-​​theory operations-​​research algo­rithms numerical-​​methods philosophy-​​of-​​engineering design-​​patterns nudge-​​targets
  • [1203.3270] Extrac­tion of Facial Fea­ture Points Using Cumu­la­tive Histogram

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

    image-​​segmentation image-​​analysis face-​​recognition algo­rithms nudge-​​targets
  • [1203.3284] Effi­cient Enu­mer­a­tion of the Directed Binary Per­fect Phy­lo­ge­nies from Incom­plete Data

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

    phy­lo­ge­net­ics inverse-​​problems genet­ics algo­rithms sta­tis­tics nudge-​​targets
  • [1203.0879] Design­ing and using prior knowl­edge for phase retrieval

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

    image-​​analysis image-​​processing learn­ing inverse-​​problems algo­rithms nudge-​​targets
  • [1203.3415] A New Approach to Count Pat­tern Motifs Using Com­bi­na­to­r­ial Techniches

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

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