Tag Archives: condensed-matter

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

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

    cladis­tics algo­rithms visu­al­iza­tion deep-​​time sta­tis­tics

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

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

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

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

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

    recur­sion stochastic-​​programming sim­u­la­tion nudge

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

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

    cohous­ing col­lab­o­ra­tion nerd-​​culture

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

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

    algo­rithms computational-​​geometry nudge-​​targets

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

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

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

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  • [1201.5440] Self-​​assembly of anisotropic soft par­ti­cles in two dimensions

    “The self assem­bly of core-​​corona discs inter­act­ing via anisotropic poten­tials is inves­ti­gated using Monte Carlo com­puter sim­u­la­tions. A min­i­mal inter­ac­tion poten­tial that incor­po­rates anisotropy in a sim­ple way is intro­duced. It con­sists in a core-​​corona archi­tec­ture in which the cen­ter of the core is shifted with respect to the cen­ter of the corona. Anisotropy can thus be tuned by pro­gres­sively shift­ing the posi­tion of the core. Despite its sim­plic­ity, the sys­tem self orga­nize in a rich vari­ety of struc­tures includ­ing stripes, tri­an­gu­lar and rec­tan­gu­lar lat­tices, and unusual plas­tic crys­tals. Our results indi­cate that the amount of anisotropy does not alter the lat­tice spac­ing and only influ­ences the type of clus­ter­ing (stripes, micells, etc.) of the indi­vid­ual particles.”

    self-​​assembly biologically-​​inspired sim­u­la­tion pattern-​​formation condensed-​​matter

  • [1201.5477] Entropy-​​growth-​​based model of emo­tion­ally charged online dialogues

    “We ana­lyze emo­tion­ally anno­tated mas­sive data from IRC (Inter­net Relay Chat) and model the dia­logues between its par­tic­i­pants by assum­ing that the dri­ving force for the dis­cus­sion is the entropy growth of emo­tional prob­a­bil­ity dis­tri­b­u­tion. This process is claimed to be cor­re­lated to the emer­gence of the power-​​law dis­tri­b­u­tion of the dis­cus­sion lengths observed in the dia­logues. We per­form numer­i­cal sim­u­la­tions based on the noticed phe­nom­e­non obtain­ing a good agree­ment with the real data. Finally, we pro­pose a method to arti­fi­cially pro­long the dura­tion of the dis­cus­sion that relies on the entropy of emo­tional prob­a­bil­ity distribution.”

    oh-​​look-​​power-​​laws flame-​​wars social-​​dynamics com­plex­ol­ogy cultural-​​dynamics

  • [1201.4955] Coor­di­na­tion, Dif­fer­en­ti­a­tion and Fair­ness in a pop­u­la­tion of coop­er­at­ing agents

    “In a recent paper, we ana­lyzed the self-​​assembly of a com­plex coop­er­a­tion net­work. The net­work was shown to approach a state, where every agent invests the same amount of resources. Nev­er­the­less, highly-​​connected agents arise that extract extra-​​ordinarily high pay­offs while con­tribut­ing com­pa­ra­bly lit­tle to any of their coop­er­a­tions. Here, we inves­ti­gate a vari­ant of the model, in which highly-​​connected agents have access to addi­tional resources. We study ana­lyt­i­cally and numer­i­cally whether these resources are invested in exist­ing col­lab­o­ra­tions, lead­ing to a fairer load dis­tri­b­u­tion, or in estab­lish­ing new col­lab­o­ra­tions, lead­ing to an even less fair dis­tri­b­u­tion of loads and payoffs.”

    col­lab­o­ra­tion social-​​capital agent-​​based network-​​theory com­plex­ol­ogy nudge-​​targets

  • [1201.5426] Con­straint Prop­a­ga­tion as Infor­ma­tion Maximization

    “Dana Scott used the par­tial order among par­tial func­tions for his math­e­mat­i­cal model of recur­sively defined func­tions. He inter­preted the par­tial order as one of infor­ma­tion con­tent. In this paper we elab­o­rate on Scott’s sug­ges­tion of regard­ing com­pu­ta­tion as a process of infor­ma­tion max­i­miza­tion by apply­ing it to the solu­tion of con­straint sat­is­fac­tion prob­lems. Here the method of con­straint prop­a­ga­tion can be inter­preted as decreas­ing uncer­tainty about the solu­tion — that is, as gain in infor­ma­tion about the solu­tion. As illus­tra­tive exam­ple we choose numer­i­cal con­straint sat­is­fac­tion prob­lems to be solved by inter­val con­straints. To facil­i­tate this approach to con­straint solv­ing we for­mu­late con­straint sat­is­fac­tion prob­lems as for­mu­las in pred­i­cate logic. This neces­si­tates extend­ing the usual seman­tics for pred­i­cate logic so that mean­ing is assigned not only to sen­tences but also to for­mu­las with free variables.”

    computer-​​science quite-​​interesting constraint-​​processing computational-​​methods

  • [1201.4459] An effi­cient par­al­lel algo­rithm for the longest path prob­lem in meshes

    “In this paper, first we give a sequen­tial linear-​​time algo­rithm for the longest path prob­lem in meshes. This algo­rithm can be con­sid­ered as an improve­ment of [13]. Then based on this sequen­tial algo­rithm, we present a constant-​​time par­al­lel algo­rithm for the prob­lem which can be run on every par­al­lel machine.”

    algo­rithms graph-​​theory computational-​​complexity nudge-​​targets

  • [1201.4417] Insta­bil­i­ties and Pat­terns in Cou­pled Reaction-​​Diffusion Layers

    “We study insta­bil­i­ties and pat­tern for­ma­tion in reaction-​​diffusion lay­ers that are dif­fu­sively cou­pled. For two-​​layer sys­tems of iden­ti­cal two-​​component reac­tions, we ana­lyze the sta­bil­ity of homo­ge­neous steady states by exploit­ing the block sym­met­ric struc­ture of the lin­ear prob­lem. There are eight pos­si­ble pri­mary bifur­ca­tion sce­nar­ios, includ­ing a Turing-​​Turing bifur­ca­tion that involves two dis­parate length scales whose ratio may be tuned via the inter-​​layer cou­pling. For sys­tems of $n$-component lay­ers and non-​​identical lay­ers, the lin­ear problem’s block form allows approx­i­mate decom­po­si­tion into lower-​​dimensional lin­ear prob­lems if the cou­pling is suf­fi­ciently weak. As an exam­ple, we apply these results to a two-​​layer Brus­se­la­tor sys­tem. The com­pet­ing length scales engi­neered within the lin­ear prob­lem are read­ily appar­ent in numer­i­cal sim­u­la­tions of the full sys­tem. Select­ing a $sqrt{2}$:1 length scale ratio pro­duces an unusual steady square pattern.”

    cute emergent-​​design pattern-​​formation com­plex­ol­ogy nudge-​​targets nonlinear-​​dynamics

  • [1201.4737] Pro­duc­tion Sys­tem Rules as Pro­tein Com­plexes from Genetic Reg­u­la­tory Networks

    “This short paper intro­duces a new way by which to design pro­duc­tion sys­tem rules. An indi­rect encod­ing scheme is pre­sented which views such rules as pro­tein com­plexes pro­duced by the tem­po­ral behav­iour of an arti­fi­cial genetic reg­u­la­tory net­work. This ini­tial study begins by using a sim­ple Boolean reg­u­la­tory net­work to pro­duce tra­di­tional ternary-​​encoded rules before mov­ing to a fuzzy vari­ant to pro­duce real-​​valued rules. Com­pet­i­tive per­for­mance is shown with related genetic reg­u­la­tory net­works and rule-​​based sys­tems on bench­mark problems.”

    evolutionary-​​algorithms production-​​systems computer-​​science emergent-​​design

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  • [1112.4323] Between the­ory and prac­tice: guide­lines for an opti­miza­tion scheme with genetic algo­rithms — Part I: single-​​objective con­tin­u­ous global optimization

    The rapid advances in the field of opti­miza­tion meth­ods in many pure and applied sci­ence pose the dif­fi­culty of keep­ing track of the devel­op­ments as well as select­ing an appro­pri­ate tech­nique that best suits the prob­lem in-​​hand. From a prac­ti­tioner point of view is right­ful to wan­der “which opti­miza­tion method is the best for my prob­lem?”. Look­ing at the opti­miza­tion process as a “sys­tem” of inter­con– nected parts, in this paper are col­lected some ideas about how to tackle an opti­miza­tion prob­lem using a class of tools from evo­lu­tion­ary com­pu­ta­tions called Genetic Algo­rithms. Despite the num­ber of opti­miza­tion tech­niques avail­able nowa­days the author of this paper thinks that Genetic Algo­rithms still play a cen­tral role for their ver­sa­til­ity, robust­ness, the­o­ret­i­cal frame­work and sim­plic­ity of use. The paper can be con­sid­ered a “col­lec­tion of tips” (from lit­er­a­ture and per­sonal expe­ri­ence) for the non-​​computer-​​scientist that has to deal with opti­miza­tion prob­lems both in the sci­ence and engi­neer­ing prac­tice. No orig­i­nal meth­ods or algo­rithms are proposed.

    meta-​​optimization pragmatism-​​almost genetic-​​algorithm agile-​​almost project-​​management

  • [1112.6075] A semi­def­i­nite pro­gram­ming approach for solv­ing Mul­ti­ob­jec­tive Lin­ear Programming

    Sev­eral algo­rithms are avail­able in the lit­er­a­ture for find­ing the entire set of Pareto-​​optimal solu­tions in Mul­ti­Ob­jec­tive Lin­ear Pro­gram­ming (MOLP). How­ever, it has not been pro­posed so far an inte­rior point algo­rithm that finds all Pareto-​​optimal solu­tions of MOLP. We present an explicit con­struc­tion, based on a trans­for­ma­tion of any MOLP into a finite sequence of Semi­Def­i­nite Pro­grams (SDP), the solu­tions of which give the entire set of Pareto-​​optimal extreme points solu­tions of MOLP. These SDP prob­lems are solved by inte­rior point meth­ods; thus our approach pro­vides a pseudo-​​polynomial inte­rior point method­ol­ogy to find the set of Pareto-​​optimal solu­tions of MOLP.

    linear-​​programming algo­rithms multiobjective-​​optimization nudge-​​targets operations-​​research

  • [1112.0826] Clus­ter­ing under Per­tur­ba­tion Resilience

    Recently, Bilu and Linial for­mal­ized an implicit assump­tion often made when choos­ing a clus­ter­ing objec­tive: that the opti­mum clus­ter­ing to the objec­tive should be pre­served under small mul­ti­plica­tive per­tur­ba­tions to dis­tances between points. They showed that for max-​​cut clus­ter­ing it is pos­si­ble to cir­cum­vent NP-​​hardness and obtain polynomial-​​time algo­rithms for instances resilient to large (fac­tor $O(sqrt{n})$) per­tur­ba­tions, and sub­se­quently Awasthi et al. con­sid­ered center-​​based objec­tives, giv­ing algo­rithms for instances resilient to O(1) fac­tor per­tur­ba­tions. In this paper, we greatly advance this line of work. For center-​​based objec­tives, we present an algo­rithm that can opti­mally clus­ter instances resilient to $(1 + sqrt{2})$-factor per­tur­ba­tions, solv­ing an open prob­lem of Awasthi et al. For a com­monly used center-​​based objec­tive $k$-median, we addi­tion­ally give algo­rithms for a more relaxed assump­tion in which we allow the opti­mal solu­tion to change in a small $epsilon$ frac­tion of the points after per­tur­ba­tion. We give the first bounds known for this more real­is­tic and more gen­eral set­ting. We also pro­vide pos­i­tive results for min-​​sum clus­ter­ing which is a gen­er­ally much harder objec­tive than $k$-median (and also non-​​center-​​based). Our algo­rithms are based on new link­age cri­te­ria that may be of inde­pen­dent inter­est. Addi­tion­ally, we give sublinear-​​time algo­rithms, show­ing algo­rithms that can return an implicit clus­ter­ing from only access to a small ran­dom sample.

    clus­ter­ing sta­tis­tics nonparametric-​​methods robust­ness resilience algo­rithms nudge-​​targets

  • [1104.3516] An adap­tive hier­ar­chi­cal domain decom­po­si­tion method for par­al­lel con­tact dynam­ics sim­u­la­tions of gran­u­lar materials

    A fully par­al­lel ver­sion of the con­tact dynam­ics (CD) method is pre­sented in this paper. For large enough sys­tems, 100% effi­ciency has been demon­strated for up to 256 proces­sors using a hier­ar­chi­cal domain decom­po­si­tion with dynamic load bal­anc­ing. The iter­a­tive scheme to cal­cu­late the con­tact forces is left domain-​​wise sequen­tial, with data exchange after each iter­a­tion step, which ensures its sta­bil­ity. The num­ber of addi­tional iter­a­tions required for con­ver­gence by the par­tially par­al­lel updates at the domain bound­aries becomes neg­li­gi­ble with increas­ing num­ber of par­ti­cles, which allows for an effec­tive par­al­leliza­tion. Com­pared to the sequen­tial imple­men­ta­tion, we found no influ­ence of the par­al­leliza­tion on sim­u­la­tion results.

    sim­u­la­tion condensed-​​matter granular-​​materials complex-​​systems

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  • [1105.4335] Phys­i­cal approaches to the dynam­ics of genetic cir­cuits: A tutorial

    “Cel­lu­lar behav­ior is gov­erned by gene reg­u­la­tory processes that are intrin­si­cally dynamic and non­lin­ear, and are sub­ject to non-​​negligible amounts of ran­dom fluc­tu­a­tions. Such con­di­tions are ubiq­ui­tous in phys­i­cal sys­tems, where they have been stud­ied for decades using the tools of sta­tis­ti­cal and non­lin­ear physics. The goal of this review is to show how approaches tra­di­tion­ally used in physics can help in reach­ing a systems-​​level under­stand­ing of liv­ing cells. To that end, we present an overview of the dynam­i­cal phe­nom­ena exhib­ited by genetic cir­cuits and their func­tional sig­nif­i­cance. We also describe the the­o­ret­i­cal and exper­i­men­tal approaches that are being used to unravel the rela­tion­ship between cir­cuit struc­ture and func­tion in dynam­i­cal cel­lu­lar processes under the influ­ence of noise, both at the single-​​cell level and in cel­lu­lar pop­u­la­tions, where inter­cel­lu­lar cou­pling plays an impor­tant role.”

    systems-​​biology biological-​​engineering genetic-​​regulatory-​​networks emergent-​​design bio­chem­istry overview

  • [1106.0371] A Novel Image Seg­men­ta­tion Enhance­ment Tech­nique based on Active Con­tour and Topo­log­i­cal Alignments

    “Topo­log­i­cal align­ments and snakes are used in image pro­cess­ing, par­tic­u­larly in locat­ing object bound­aries. Both of them have their own advan­tages and lim­i­ta­tions. To improve the over­all image bound­ary detec­tion sys­tem, we focused on devel­op­ing a novel algo­rithm for image pro­cess­ing. The algo­rithm we pro­pose to develop will based on the active con­tour method in con­junc­tion with topo­log­i­cal align­ments method to enhance the image detec­tion approach. The algo­rithm presents novel tech­nique to incor­po­rate the advan­tages of both Topo­log­i­cal Align­ments and snakes. Where the ini­tial seg­men­ta­tion by Topo­log­i­cal Align­ments is firstly trans­formed into the input of the snake model and begins its evolve­ment to the inter­ested object bound­ary. The results show that the algo­rithm can deal with low con­trast images and shape cells, demon­strate the seg­men­ta­tion accu­racy under weak image bound­aries, which respon­si­ble for lack­ing accu­racy in image detect­ing tech­niques. We have achieved bet­ter seg­men­ta­tion and bound­ary detect­ing for the image, also the abil­ity of the sys­tem to improve the low con­trast and deal with over and under segmentation.”

    image-​​segmentation algo­rithms nudge-​​targets

  • [1106.2508] A Prac­ti­cal Imple­men­ta­tion of the Bernoulli Factory

    “…While sev­eral prac­ti­cal uses of the method have been pro­posed in Monte Carlo appli­ca­tions, these require an imple­men­ta­tion frame­work that is flex­i­ble, gen­eral and effi­cient. We present such a frame­work for func­tions that are either strictly lin­ear, con­cave, or con­vex on the unit inter­val using a series of enve­lope func­tions defined through a cas­cade, and show that this method not only greatly reduces the num­ber of input bits needed in prac­tice com­pared to other cur­rently pro­posed solu­tions for more spe­cific prob­lems, but can eas­ily be cou­pled to more asymp­tot­i­cally effi­cient meth­ods to allow for the­o­ret­i­cally strong results.”

    algo­rithms numerical-​​methods Monte-​​Carlo-​​simulation probability-​​theory nudge-​​targets

  • [1105.1729] Evo­lu­tion­ary search for novel super­hard materials

    “We have devel­oped a method for pre­dic­tion of the hard­est crys­tal struc­tures in a given chem­i­cal sys­tem. It is based on the evo­lu­tion­ary algo­rithm USPEX and electronegativity-​​based hard­ness model that we have aug­mented with bond-​​valence model and graph the­ory. These exten­sions enable cor­rect descrip­tion of the hard­ness of lay­ered, mol­e­c­u­lar and low-​​symmetry crys­tal struc­tures. Apply­ing this method to C and TiO2, we have (i) obtained a num­ber of low-​​energy car­bon struc­tures with hard­ness slightly lower than dia­mond and (ii) proved that TiO2 in any of its pos­si­ble poly­morphs can­not be the hard­est oxide, its hard­ness being below 17 GPa.”

    materials-​​science genetic-​​algorithm condensed-​​matter sim­u­la­tion nudge-​​targets

  • [1109.0573] Phase Retrieval via Matrix Completion

    “This paper con­sid­ers the fun­da­men­tal prob­lem of recov­er­ing a gen­eral sig­nal, an image for exam­ple, from the mag­ni­tude of its Fourier trans­form. This prob­lem, also known as phase retrieval, arises in many appli­ca­tions and has chal­lenged engi­neers, physi­cists, and math­e­mati­cians for decades. Its ori­gin comes from the fact that detec­tors can often times only record the squared mod­u­lus of the Fres­nel or Fraun­hofer dif­frac­tion pat­tern of the radi­a­tion that is scat­tered from an object. In such set­tings, one can­not mea­sure the phase of the opti­cal wave reach­ing the detec­tor and, there­fore, much infor­ma­tion about the scat­tered object or the opti­cal field is lost since, as is well known, the phase encodes a lot of the struc­tural con­tent of the image we wish to form.”

    image-​​processing inverse-​​problems signal-​​processing system-​​identification frequency-​​space algo­rithms nudge-​​targets numerical-​​methods

  • [1109.0807] Har­monic Analy­sis of Boolean Net­works: Deter­mi­na­tive Power and Perturbations

    “Con­sider a large Boolean net­work with a feed for­ward struc­ture. Given a prob­a­bil­ity dis­tri­b­u­tion for the inputs, can one find-​​possibly small-​​collections of input nodes that deter­mine the states of most other nodes in the network?…”

    Boolean-​​networks Kauff­ma­nia com­plex­ol­ogy discrete-​​mathematics mathematical-​​recreations nudge-​​targets

  • [0801.0830] Evo­lu­tion of cen­tral pat­tern gen­er­a­tors for the con­trol of a five-​​link bipedal walk­ing mechanism

    “With the aim of pro­duc­ing a sta­ble human-​​like bipedal gait, a five-​​link pla­nar walk­ing mech­a­nism is cou­pled with a cen­tral pat­tern gen­er­a­tor (CPG) neural net­work, con­sist­ing of units based on Matsuoka’s half-​​center oscil­la­tor model with a firm basis in neu­ro­phys­i­ol­ogy. As a min­i­mal­is­tic approach to bipedal walk­ing, this type of walk­ing mech­a­nism con­tains only four actu­a­tors, and is lack­ing feet and ankles. The mech­a­nism is sim­u­lated with accu­rate physics, allow­ing real­is­tic fit­ness eval­u­a­tions for the cre­ation of CPG con­trollers through evo­lu­tion­ary com­pu­ta­tion. The oscil­la­tory para­me­ters, inter­nal con­nec­tiv­ity struc­ture, and exter­nal feed­back path­ways of the net­works are deter­mined through genetic algo­rithms (GA) opti­miza­tion. The evolved CPG net­works are trans­ferred to a hard­ware imple­men­ta­tion of the mech­a­nism, to test their per­for­mance under real-​​world dynam­ics. Results con­firm that the bio­log­i­cally inspired CPG model is very well suited for con­trol­ling legged loco­mo­tion, since a diverse man­i­fes­ta­tion of CPG net­works (with and with­out exter­nal feed­back) have been observed to suc­ceed dur­ing the course of GA eval­u­a­tions. Obser­va­tions also imply that while the CPG mech­a­nism is inher­ently able to sus­tain a sta­ble gait, the uti­liza­tion of feed­back path­ways makes the gait more human-​​like and is needed to pro­vide a means to adapt to irreg­u­lar­i­ties in the environment.”

    robot­ics engineering-​​design genetic-​​algorithm neural-​​networks cyber­net­ics nudge-​​targets

  • [1109.3351] Phys­i­cal lim­its on coop­er­a­tive protein-​​DNA bind­ing and the kinet­ics of com­bi­na­to­r­ial tran­scrip­tion regulation

    “Much of the com­plex­ity observed in gene reg­u­la­tion orig­i­nates from coop­er­a­tive protein-​​DNA bind­ing. While stud­ies of the tar­get search of pro­teins for their spe­cific bind­ing sites on the DNA have revealed design prin­ci­ples for the quan­ti­ta­tive char­ac­ter­is­tics of protein-​​DNA inter­ac­tions, no such prin­ci­ples are known for the coop­er­a­tive inter­ac­tions between DNA-​​binding pro­teins. We con­sider a sim­ple the­o­ret­i­cal model for two inter­act­ing tran­scrip­tion fac­tor (TF) species, search­ing for and bind­ing to two adja­cent tar­get sites hid­den in the genomic back­ground. We study the kinetic com­pe­ti­tion of a dimer search path­way and a monomer search path­way, as well as the steady-​​state reg­u­la­tion func­tion medi­ated by the two TFs over a broad range of TF-​​TF inter­ac­tion strengths. Using a tran­scrip­tional AND-​​logic as exem­plary func­tional con­text, we iden­tify the func­tion­ally desir­able regime for the inter­ac­tion. We find that both weak and very strong TF-​​TF inter­ac­tions are favor­able, albeit with dif­fer­ent char­ac­ter­is­tics. How­ever, there is also an unfa­vor­able regime of inter­me­di­ate inter­ac­tions where the genetic response is pro­hib­i­tively slow.”

    biological-​​engineering genetic-​​regularory-​​networks systems-​​biology emergent-​​design nudge-​​targets

  • [1109.6874] #h00t: Cen­sor­ship Resis­tant Microblogging

    “Microblog­ging ser­vices such as Twit­ter are an increas­ingly impor­tant way to com­mu­ni­cate, both for indi­vid­u­als and for groups through the use of hash­tags that denote top­ics of con­ver­sa­tion. How­ever, groups can be eas­ily blocked from com­mu­ni­cat­ing through block­ing of posts with the given hash­tags. We pro­pose #h00t, a sys­tem for cen­sor­ship resis­tant microblog­ging. #h00t presents an inter­face that is much like Twit­ter, except that hash­tags are replaced with very short hashes (e.g., 24 bits) of the group iden­ti­fier. Nat­u­rally, with such short hashes, hash­tags from dif­fer­ent groups may col­lide and #h00t users will actu­ally seek to cre­ate col­li­sions. By encrypt­ing all posts with keys derived from the group iden­ti­fiers, #h00t client soft­ware can fil­ter out other groups’ posts while mak­ing such fil­ter­ing dif­fi­cult for the adver­sary. In essence, by lever­ag­ing col­li­sions, groups can tun­nel their posts in other groups’ posts. A cen­sor could not block a given group with­out also block­ing the other groups with col­lid­ing hash­tags. We eval­u­ate the fea­si­bil­ity of #h00t through traces col­lected from Twit­ter, show­ing that a sin­gle mod­ern com­puter has enough com­pu­ta­tional through­put to encrypt every tweet sent through Twit­ter in real time. We also use these traces to ana­lyze the band­width and anonymity trade­offs that would come with dif­fer­ent vari­a­tions on how group iden­ti­fiers are encoded and hash­tags are selected to pur­pose­fully col­lide with one another.”

    social-​​media steganog­ra­phy robust­ness activism cute

  • [1107.0414] A ran­dom walk on image patches

    “In this paper we address the prob­lem of under­stand­ing the suc­cess of algo­rithms that orga­nize patches accord­ing to graph-​​based met­rics. Algo­rithms that ana­lyze patches extracted from images or time series have led to state-​​of-​​the art tech­niques for clas­si­fi­ca­tion, denois­ing, and the study of non­lin­ear dynam­ics. The main con­tri­bu­tion of this work is to pro­vide a the­o­ret­i­cal expla­na­tion for the above exper­i­men­tal obser­va­tions. Our approach relies on a detailed analy­sis of the com­mute time met­ric on pro­to­typ­i­cal graph mod­els that epit­o­mize the geom­e­try observed in gen­eral patch graphs.…”

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

  • [1107.0385] An algo­rithm for autonomously plot­ting solu­tion sets in the pres­ence of turn­ing points

    “Plot­ting solu­tion sets for par­tic­u­lar equa­tions may be com­pli­cated by the exis­tence of turn­ing points. Here we describe an algo­rithm which not only over­comes such prob­lem­atic points, but does so in the most gen­eral of set­tings. Appli­ca­tions of the algo­rithm are high­lighted through two exam­ples: the first pro­vides ver­i­fi­ca­tion, while the sec­ond demon­strates a non-​​trivial appli­ca­tion. The lat­ter is fol­lowed by a thor­ough run-​​time analy­sis. While both exam­ples deal with bivari­ate equa­tions, it is dis­cussed how the algo­rithm may be gen­er­al­ized for space curves in $R^{3}$.”

    visu­al­iza­tion math­e­mat­ics graph­ics approx­i­ma­tion algo­rithms nudge-​​targets

  • [1105.1033] Adap­tively Learn­ing the Crowd Kernel

    “We intro­duce an algo­rithm that, given n objects, learns a sim­i­lar­ity matrix over all n^2 pairs, from crowd­sourced data alone. The algo­rithm sam­ples responses to adap­tively cho­sen triplet-​​based relative-​​similarity queries. Each query has the form “is object ‘a’ more sim­i­lar to ‘b’ or to ‘c’?” and is cho­sen to be max­i­mally infor­ma­tive given the pre­ced­ing responses. The out­put is an embed­ding of the objects into Euclid­ean space (like MDS); we refer to this as the “crowd ker­nel.” SVMs reveal that the crowd ker­nel cap­tures promi­nent and sub­tle fea­tures across a num­ber of domains, such as “is striped” among neck­ties and “vowel vs. con­so­nant” among letters.”

    clas­si­fi­ca­tion ontology-​​discovery crowd­sourc­ing feature-​​extraction algo­rithms nudge-​​targets performance-​​space-​​analysis

  • [1109.1030] An Algo­rithm for Detect­ing Intrin­si­cally Knot­ted Graphs

    “We describe an algo­rithm that rec­og­nizes some (per­haps all) intrin­si­cally knot­ted (IK) graphs, and can help find knot­less embed­dings for graphs that are not IK. The algo­rithm, imple­mented as a Math­e­mat­ica pro­gram, has already been used by Gold­berg, Mattman, and Naimi [6] to greatly expand the list of known minor min­i­mal IK graphs, and to find knot­less embed­dings for some graphs that had pre­vi­ously resisted attempts to clas­sify them as IK or non-​​IK.”

    com­bi­na­torics topol­ogy algo­rithms nudge-​​targets

  • [1109.5635] Approx­i­mat­ing Edit Dis­tance in Near-​​Linear Time

    “We show how to com­pute the edit dis­tance between two strings of length n up to a fac­tor of 2^{~O(sqrt(log n))} in n^(1+o(1)) time. This is the first sub-​​polynomial approx­i­ma­tion algo­rithm for this prob­lem that runs in near-​​linear time, improv­ing on the state-​​of-​​the-​​art n^(1/3+o(1)) approx­i­ma­tion. Pre­vi­ously, approx­i­ma­tion of 2^{~O(sqrt(log n))} was known only for embed­ding edit dis­tance into l_​1, and it is not known if that embed­ding can be com­puted in less than qua­dratic time.”

    algo­rithms string-​​editing Levenshtein-​​distance rewriting-​​systems bioin­for­mat­ics nudge-​​targets

  • [1107.1866] Priority-​​based task reas­sign­ments in hier­ar­chi­cal 2D mesh-​​connected sys­tems using tableaux

    “Task reas­sign­ments in 2D mesh-​​connected sys­tems (2D-​​MSs) have been researched and sim­u­lated for sev­eral decades. We pro­pose a hier­ar­chi­cal 2D mesh-​​connected sys­tem (2D-​​HMS) in order to exploit the reg­u­lar nature of a 2D-​​MS. In our approach priority-​​based task assign­ments and reas­sign­ments in a 2D-​​HMS are rep­re­sented by tableaux and their algo­rithms. We pro­vide exam­ples of priority-​​based task reas­sign­ments in a 2D-​​HMS in which task relo­ca­tions are sim­ply reduced to a jeu de taquin slide.”

    sched­ul­ing operations-​​research algo­rithms grid-​​computing opti­miza­tion nudge-​​targets

  • [1101.4744] Clus­ter­ing func­tional data using wavelets

    “We present two meth­ods for detect­ing pat­terns and clus­ters in high dimen­sional time-​​dependent func­tional data. Our meth­ods are based on wavelet-​​based sim­i­lar­ity mea­sures, since wavelets are well suited for iden­ti­fy­ing highly dis­crim­i­nant local time and scale fea­tures. The mul­tires­o­lu­tion aspect of the wavelet trans­form pro­vides a time-​​scale decom­po­si­tion of the sig­nals allow­ing to visu­al­ize and to clus­ter the func­tional data into homo­ge­neous groups. For each input func­tion, through its empir­i­cal orthog­o­nal wavelet trans­form the first method uses the dis­tri­b­u­tion of energy across scales gen­er­ate a handy num­ber of fea­tures that can be suf­fi­cient to still make the sig­nals well dis­tin­guish­able. Our new sim­i­lar­ity mea­sure com­bined with an effi­cient fea­ture selec­tion tech­nique in the wavelet domain is then used within more or less clas­si­cal clus­ter­ing algo­rithms to effec­tively dif­fer­en­ti­ate among high dimen­sional pop­u­la­tions. The sec­ond method uses dis­sim­i­lar­ity mea­sures between the whole time-​​scale rep­re­sen­ta­tions and are based on wavelet-​​coherence tools. The clus­ter­ing is then per­formed using a k-​​centroid algo­rithm start­ing from these dis­sim­i­lar­i­ties. Prac­ti­cal per­for­mance of these meth­ods that jointly designs both the fea­ture selec­tion in the wavelet domain and the clas­si­fi­ca­tion dis­tance is demon­strated through sim­u­la­tions as well as daily pro­files of the French elec­tric­ity power demand.”

    clas­si­fi­ca­tion time-​​series feature-​​extraction machine-​​learning multiobjective-​​optimization ontology-​​discovery wavelets nudge-​​targets

  • [1105.3726] Con­trol­ling Com­plex Net­works with Com­pen­satory Perturbations

    “The response of com­plex net­works to per­tur­ba­tions is of utmost impor­tance in areas as diverse as ecosys­tem man­age­ment, emer­gency response, and cell repro­gram­ming. A fun­da­men­tal prop­erty of net­works is that the per­tur­ba­tion of one node can affect other nodes, in a process that may cause the entire or sub­stan­tial part of the sys­tem to change behav­ior and pos­si­bly col­lapse. Recent research in meta­bolic and food-​​web net­works has demon­strated the con­cept that net­work dam­age caused by exter­nal per­tur­ba­tions can often be mit­i­gated or reversed by the appli­ca­tion of com­pen­satory per­tur­ba­tions. Com­pen­satory per­tur­ba­tions are con­strained to be phys­i­cally admis­si­ble and amenable to imple­men­ta­tion on the net­work. How­ever, the sys­tem­atic iden­ti­fi­ca­tion of com­pen­satory per­tur­ba­tions that con­form to these con­straints remains an open prob­lem. Here, we present a method to con­struct com­pen­satory per­tur­ba­tions that can con­trol the fate of gen­eral net­works under such con­straints. Our approach accounts for the full non­lin­ear behav­ior of real com­plex net­works and can bring the sys­tem to a desir­able tar­get state even when this state is not directly acces­si­ble. Appli­ca­tions to genetic net­works show that com­pen­satory per­tur­ba­tions are effec­tive even when lim­ited to a small frac­tion of all nodes in the net­work and that they are far more effec­tive when lim­ited to the highest-​​degree nodes. The approach is con­cep­tu­ally sim­ple and com­pu­ta­tion­ally effi­cient, mak­ing it suit­able for the res­cue, con­trol, and repro­gram­ming of large com­plex net­works in var­i­ous domains.”

    emergent-​​design com­plex­ol­ogy con­trol biological-​​engineering nudge-​​targets

  • [1109.1275] A For­mal Ver­i­fi­ca­tion Approach to the Design of Syn­thetic Gene Networks

    “The design of genetic net­works with spe­cific func­tions is one of the major goals of syn­thetic biol­ogy. How­ever, con­struct­ing bio­log­i­cal devices that work “as required” remains chal­leng­ing, while the cost of uncov­er­ing flawed designs exper­i­men­tally is large. To address this issue, we pro­pose a fully auto­mated frame­work that allows the cor­rect­ness of syn­thetic gene net­works to be for­mally ver­i­fied in sil­ico from rich, high level func­tional spec­i­fi­ca­tions. Given a device, we auto­mat­i­cally con­struct a math­e­mat­i­cal model from exper­i­men­tal data char­ac­ter­iz­ing the parts it is com­posed of. The spe­cific model struc­ture guar­an­tees that all exper­i­men­tal obser­va­tions are cap­tured and allows us to con­struct finite abstrac­tions through poly­he­dral oper­a­tions. The cor­rect­ness of the model with respect to tem­po­ral logic spec­i­fi­ca­tions can then be ver­i­fied auto­mat­i­cally using meth­ods inspired by model check­ing. Over­all, our pro­ce­dure is con­ser­v­a­tive but it can fil­ter through a large num­ber of poten­tial device designs and select few that sat­isfy the spec­i­fi­ca­tion to be imple­mented and tested fur­ther exper­i­men­tally. Illus­tra­tive exam­ples of the appli­ca­tion of our meth­ods to the design of sim­ple syn­thetic gene net­works are included.”

    genetic-​​regulatory-​​networks bioin­for­mat­ics biological-​​engineering design-​​automation emergent-​​design acceptance-​​testing performance-​​measure nudge

  • [1108.1150] Epis­ta­sis can lead to frag­mented neu­tral spaces and con­tin­gency in evolution

    “Under neu­tral rec­i­p­ro­cal sign epis­ta­sis, two genetic changes are jointly neu­tral, even though their indi­vid­ual effects are dele­te­ri­ous. By using the widely stud­ied map­ping from an RNA sequence to sec­ondary struc­ture, we inves­ti­gate the effect of this kind of epis­ta­sis on neu­tral spaces cor­re­spond­ing to net­works of geno­types that fold to the same sec­ondary struc­ture. Neu­tral net­works for RNA struc­tures with n bonds are typ­i­cally frag­mented into at least 2n dif­fer­ent neu­tral com­po­nents that can­not be con­nected by sin­gle point muta­tions. By exhaus­tive enu­mer­a­tion of all RNA sec­ondary struc­tures of sequences of length 15 we show that most net­works are not dom­i­nated by one neu­tral com­po­nent, but are rather bro­ken up into mul­ti­ple large com­po­nents. Although they gen­er­ate the same phe­no­type, com­po­nents of a sin­gle neu­tral net­work are het­ero­ge­neous, show­ing wide vari­a­tions in their robust­ness and their evolv­abil­ity. Both prop­er­ties are cor­re­lated with com­po­nent size, rather than with the size of their under­ly­ing neu­tral net­work. In par­tic­u­lar, sets of acces­si­ble phe­no­types can vary quite strongly between com­po­nents. Thus, the poten­tial for future inno­va­tion is con­tin­gent on which neu­tral com­po­nent a pop­u­la­tion occu­pies. We fur­ther argue that neu­tral rec­i­p­ro­cal sign epis­ta­sis may have sim­i­lar con­se­quences for neu­tral evo­lu­tion of other bio­log­i­cal sys­tems as well.”

    com­bi­na­torics RNA neutral-​​networks com­plex­ol­ogy bioin­for­mat­ics polymer-​​models mathematical-​​recreations nudge-​​targets

  • Old​Fonts​.com | About Us

    “Will­son founded 3IP in 1989 to self-​​publish a book of pre­ten­tious nature essays. Soon after, he found him­self tin­ker­ing with type design, and 3IP has since become known for its library of authentic-​​looking hand­writ­ing fonts—many of them mod­eled after his­tor­i­cal penmanship—and antique text simulations.”

    typog­ra­phy fonts hand­writ­ing

  • Col­lec­tive Wis­dom — Crooked Timber

    “More broadly, a sim­ple dic­tum such as ‘lis­ten to the experts’ isn’t going to work, pre­cisely because our most pow­er­ful meth­ods of gen­er­at­ing new knowl­edge (viz. the sci­ences) are not so much based on lis­ten­ing to indi­vid­ual experts, as on includ­ing these experts (and many oth­ers) in broader social sys­tems which expose them con­tin­u­ally to the ideas of oth­ers and vice-​​versa. Design­ing (or – per­haps bet­ter– nur­tur­ing) such sys­tems is hard to think about and hard to do – but it has to be the way forward.”

    via:arsyed wisdom-​​of-​​crowds com­plex­ol­ogy inno­va­tion cultural-​​assumptions cre­den­tial­ing problem-​​solving what-​​is-​​true-​​is-​​what-​​gets-​​said

  • [1109.1146] A Dis­trib­uted Mincut/​Maxflow Algo­rithm Com­bin­ing Path Aug­men­ta­tion and Push-​​Relabel

    “We develop a novel dis­trib­uted algo­rithm for the min­i­mum cut prob­lem. We pri­mar­ily aim at solv­ing large sparse prob­lems. Assum­ing ver­tices of the graph are par­ti­tioned into sev­eral regions, the algo­rithm per­forms path aug­men­ta­tions inside the regions and updates of the push-​​relabel style between the regions. The inter­ac­tion between regions is con­sid­ered expen­sive (regions are loaded into the mem­ory one-​​by-​​one or located on sep­a­rate machines in a net­work). The algo­rithm works in sweeps — passes over all regions. Let $B$ be the set of ver­tices inci­dent to inter-​​region edges of the graph. We present a sequen­tial and par­al­lel ver­sions of the algo­rithm which ter­mi­nate in at most $2|B|^2+1$ sweeps. The com­pet­ing algo­rithm by Delong and Boykov uses push-​​relabel updates inside regions. In the case of a fixed par­ti­tion we prove that this algo­rithm has a tight $O(n^2)$ bound on the num­ber of sweeps, where $n$ is the num­ber of ver­tices. We tested sequen­tial ver­sions of the algo­rithms on instances of maxflow prob­lems in com­puter vision. Exper­i­men­tally, the num­ber of sweeps required by the new algo­rithm is much lower than for the Delong and Boykov’s vari­ant. Large prob­lems (up to $108$ ver­tices and $6cdot 108$ edges) are solved using under 1GB of mem­ory in about 10 sweeps.”

    algo­rithms operations-​​research nudge-​​targets

  • [1105.4953] A fast near­est neigh­bor search algo­rithm based on vec­tor quantization

    “In this arti­cle, we pro­pose a new fast near­est neigh­bor search algo­rithm, based on vec­tor quan­ti­za­tion. Like many other branch and bound search algo­rithms [1,10], a pre­pro­cess­ing recur­sively par­ti­tions the data set into dis­jointed sub­sets until the num­ber of points in each part is small enough. In doing so, a search-​​tree data struc­ture is built. This pre­lim­i­nary recur­sive data-​​set par­ti­tion is based on the vec­tor quan­ti­za­tion of the empir­i­cal dis­tri­b­u­tion of the ini­tial data-​​set. Unlike pre­vi­ously cited meth­ods, this kind of par­ti­tions does not a pri­ori allow to elim­i­nate sev­eral brother nodes in the search tree with a sin­gle test. To over­come this dif­fi­culty, we pro­pose an algo­rithm to reduce the num­ber of tested brother nodes to a min­i­mal list that we call “friend Voronoi cells”. The com­plete descrip­tion of the method requires a deeper insight into the prop­er­ties of Delau­nay tri­an­gu­la­tions and Voronoi diagrams”

    algo­rithms search-​​algorithms data-​​analysis nudge-​​targets

  • [1108.0986] A prox­i­mal point algo­rithm for sequen­tial fea­ture extrac­tion applications

    “We pro­pose a prox­i­mal point algo­rithm to solve LAROS prob­lem, that is the prob­lem of find­ing a “large approx­i­mately rank-​​one sub­ma­trix”. This LAROS prob­lem is used to sequen­tially extract fea­tures in data. We also develop a new stop­ping cri­te­rion for the prox­i­mal point algo­rithm, which is based on the dual­ity con­di­tions of eps-​​optimal solu­tions of the LAROS prob­lem, with a the­o­ret­i­cal guar­an­tee. We test our algo­rithm with two image data­bases and show that we can use the LAROS prob­lem to extract appro­pri­ate com­mon fea­tures from these images.”

    algo­rithms image-​​segmentation feature-​​extraction nudge-​​targets

  • [1011.2348] Ergodic Con­trol and Poly­he­dral approaches to PageR­ank Optimization

    “We study a gen­eral class of PageR­ank opti­miza­tion prob­lems which con­sist in find­ing an opti­mal out­link strat­egy for a web site sub­ject to design con­straints. We con­sider both a con­tin­u­ous prob­lem, in which one can choose the inten­sity of a link, and a dis­crete one, in which in each page, there are oblig­a­tory links, fac­ul­ta­tive links and for­bid­den links. We show that the con­tin­u­ous prob­lem, as well as its dis­crete vari­ant when there are no con­straints cou­pling dif­fer­ent pages, can both be mod­eled by con­strained Markov deci­sion processes with ergodic reward, in which the web­mas­ter deter­mines the tran­si­tion prob­a­bil­i­ties of web­surfers. Although the num­ber of actions turns out to be expo­nen­tial, we show that an asso­ci­ated poly­tope of tran­si­tion mea­sures has a con­cise rep­re­sen­ta­tion, from which we deduce that the con­tin­u­ous prob­lem is solv­able in poly­no­mial time, and that the same is true for the dis­crete prob­lem when there are no cou­pling con­straints. We also pro­vide effi­cient algo­rithms, adapted to very large net­works. Then, we inves­ti­gate the qual­i­ta­tive fea­tures of opti­mal out­link strate­gies, and iden­tify in par­tic­u­lar assump­tions under which there exists a “mas­ter” page to which all con­trolled pages should point. We report numer­i­cal results on frag­ments of the real web graph.”

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