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

• [0912.1523] Deco­her­ence in Search Algorithms

  “Recently sev­eral quan­tum search algo­rithms based on quan­tum walks were pro­posed. Those algo­rithms dif­fer from Grover’s algo­rithm in many aspects. The goal is to find a marked ver­tex in a graph faster than clas­si­cal algo­rithms. Since the imple­men­ta­tion of those new algo­rithms in quan­tum com­put­ers or in other quan­tum devices is error-​​prone, it is impor­tant to ana­lyze their robust­ness under deco­her­ence. In this work we ana­lyze the impact of deco­her­ence on quan­tum search algo­rithms imple­mented on two-​​dimensional grids and on hypercubes.”

  quan­tums search-​​algorithms robust­ness nudge-​​targets

• The Man­i­fest Des­tiny of Arti­fi­cial Intel­li­gence » Amer­i­can Scientist

  “Arti­fi­cial intel­li­gence began with an ambi­tious research agenda: To endow machines with some of the traits we value most highly in ourselves—the fac­ulty of rea­son, skill in solv­ing prob­lems, cre­ativ­ity, the capac­ity to learn from expe­ri­ence. Early results were promis­ing. Com­put­ers were pro­grammed to play check­ers and chess, to prove the­o­rems in geom­e­try, to solve anal­ogy puz­zles from IQ tests, to rec­og­nize let­ters of the alpha­bet. Mar­vin Min­sky, one of the pio­neers, declared in 1961: “We are on the thresh­old of an era that will be strongly influ­enced, and quite pos­si­bly dom­i­nated, by intel­li­gent problem-​​solving machines.””

  artificial-​​intelligence nudge-​​book cultural-​​assumptions degenerate-​​research-​​programmes

• About « Digress​.it

  “Digress​.it is a Word­Press plu­gin that offers paragraph-​​level com­ment­ing in the mar­gins of a text. Digress​.it is geared toward in-​​depth dis­cus­sions of longer doc­u­ments: arti­cle, essay or even book-​​length.

  Blogs aren’t bad for hav­ing con­ver­sa­tions, but com­ments tend to get unwieldy, and can feel detached when the orig­i­nal post is long. To solve this, Digress​.it lets you run blog-​​style com­ment threads — digres­sions, if you will — off of indi­vid­ual para­graphs. To do this effi­ciently, we’ve re-​​imagined the con­ven­tional post-​​discussion hier­ar­chy of blogs, mov­ing the com­ment area from beneath the post to beside it (float­ing to the right) — hear­ken­ing back to the age-​​old prac­tice of scrib­bling in page mar­gins. We see great pos­si­bil­i­ties for edu­ca­tors, lit­er­ary groups, polit­i­cal or civic activists, legal schol­ars, and pretty much any­one who wants to do a com­mu­nal read­ing and encour­age discussion.”

  blog­ging social-​​media con­ver­sa­tion pub­lish­ing word­press

• [1205.3676] Con­sen­sus of Multi-​​Agent Net­works in the Pres­ence of Adver­saries Using Only Local Information

  “This paper addresses the prob­lem of resilient con­sen­sus in the pres­ence of mis­be­hav­ing nodes. Although it is typ­i­cal to assume knowl­edge of at least some non­lo­cal infor­ma­tion when study­ing secure and fault-​​tolerant con­sen­sus algo­rithms, this assump­tion is not suit­able for large-​​scale dynamic net­works. To rem­edy this, we empha­size the use of local strate­gies to deal with resilience to secu­rity breaches. We study a con­sen­sus pro­to­col that uses only local infor­ma­tion and we con­sider worst-​​case secu­rity breaches, where the com­pro­mised nodes have full knowl­edge of the net­work and the inten­tions of the other nodes. We pro­vide nec­es­sary and suf­fi­cient con­di­tions for the nor­mal nodes to reach con­sen­sus despite the influ­ence of the mali­cious nodes under dif­fer­ent threat assump­tions. These con­di­tions are stated in terms of a novel graph-​​theoretic prop­erty referred to as net­work robustness.”

  agent-​​based game-​​theory network-​​theory social-​​dynamics nudge-​​targets algo­rithms

• [1205.2604] The Infi­nite Latent Events Model

  “We present the Infi­nite Latent Events Model, a non­para­met­ric hier­ar­chi­cal Bayesian dis­tri­b­u­tion over infi­nite dimen­sional Dynamic Bayesian Net­works with binary state rep­re­sen­ta­tions and noisy-​​OR-​​like tran­si­tions. The dis­tri­b­u­tion can be used to learn struc­ture in dis­crete time­series data by simul­ta­ne­ously infer­ring a set of latent events, which events fired at each timestep, and how those events are causally linked. We illus­trate the model on a sound fac­tor­iza­tion task, a net­work topol­ogy iden­ti­fi­ca­tion task, and a video game task.”

  seems-​​familiar-​​somehow

• Space­Funcs­Doc — OpenOpt

  “The fol­low­ing geom­e­try objects are imple­mented for now in Space­Funcs mod­ule:
  Point, Line, Line­Seg­ment, Cir­cle, Plane, Tri­an­gle, Poly­gon, Sphere, Poly­tope, Poly­he­dron, Tetra­he­dron. Some more are intended to be done in next Space­Funcs release.”

  geom­e­try nudge-​​targets numerical-​​methods libraries

• GitHub does dot­files — dot​files​.github​.com

  “Why would I want my dot­files on GitHub?”

  system-​​administration Unix GitHub hints tuto­r­ial

• [1205.4591] ForeCA: Fore­castable Com­po­nent Analysis

  “Blind source sep­a­ra­tion (BSS) tech­niques are often applied to mul­ti­vari­ate time series with the goal to obtain bet­ter fore­casts. But BSS and the need for bet­ter fore­casts are often treated sep­a­rately, in the sense that find­ing an opti­mally trans­formed (sub-)space has noth­ing to do with the aim to pre­dict well. Here I intro­duce Fore­castable Com­po­nent Analy­sis (ForeCA), a new BSS tech­nique for tem­po­rally depen­dent sig­nals that uses fore­casta­bil­ity as the explicit objec­tive in find­ing an opti­mal trans­for­ma­tion. It sep­a­rates the sig­nal into the fore­castable, $mathbf{F}$, and the orthog­o­nal white noise space, $mathbf{F}^{bot}$. Sim­u­la­tions and appli­ca­tions to finan­cial data show that ForeCA suc­cess­fully finds sig­nals that can be used to fore­cast. ForeCA there­fore auto­mat­i­cally dis­cov­ers infor­ma­tive struc­ture in mul­ti­vari­ate sig­nals. The R pack­age (this http URL) will be pub­licly avail­able on CRAN upon pub­li­ca­tion of the manuscript.”

  sta­tis­tics algo­rithms component-​​analysis pre­dic­tion

• [1201.5597] The mate-​​in-​​n prob­lem of infi­nite chess is decidable

  “…The proof pro­ceeds by show­ing that the mate-​​in-​​n prob­lem is express­ible in what we call the first-​​order struc­ture of chess, which we prove (in the rel­e­vant frag­ment) is an auto­matic struc­ture, whose the­ory is there­fore decid­able. Indeed, it is defin­able in Pres­burger arith­metic. Unfor­tu­nately, this res­o­lu­tion of the mate-​​in-​​n prob­lem does not appear to set­tle the decid­abil­ity of the more gen­eral winning-​​position prob­lem, the prob­lem of deter­min­ing whether a des­ig­nated player has a win­ning strat­egy from a given posi­tion, since a posi­tion may admit a win­ning strat­egy with­out any bound on the num­ber of moves required. This issue is con­nected with trans­fi­nite game val­ues in infi­nite chess, and the exact value of the omega one of chess is not known.”

  mathematical-​​recreations game-​​theory proof­ing games unde­cid­abil­ity

• [1203.5351] Activ­ity dri­ven mod­el­ing of dynamic networks

  “Net­work mod­el­ing plays a crit­i­cal role in iden­ti­fy­ing sta­tis­ti­cal reg­u­lar­i­ties and struc­tural prin­ci­ples com­mon to many sys­tems. The large major­ity of recent mod­el­ing approaches are con­nec­tiv­ity dri­ven. The struc­tural pat­terns of the net­work are at the basis of the mech­a­nisms rul­ing the net­work for­ma­tion. Con­nec­tiv­ity dri­ven mod­els nec­es­sar­ily pro­vide a time-​​aggregated rep­re­sen­ta­tion that may fail to describe the instan­ta­neous and fluc­tu­at­ing dynam­ics of many net­works. We address this chal­lenge by defin­ing the activ­ity poten­tial, a time invari­ant func­tion char­ac­ter­iz­ing the agents’ inter­ac­tions and con­struct­ing an activ­ity dri­ven model capa­ble of encod­ing the instan­ta­neous time descrip­tion of the net­work dynam­ics. The model pro­vides an expla­na­tion of struc­tural fea­tures such as the pres­ence of hubs, which sim­ply orig­i­nate from the het­ero­ge­neous activ­ity of agents. Within this frame­work, highly dynam­i­cal net­works can be described ana­lyt­i­cally, allow­ing a quan­ti­ta­tive dis­cus­sion of the biases induced by the time-​​aggregated rep­re­sen­ta­tions in the analy­sis of dynam­i­cal processes.”

  network-​​theory infer­ence sta­tis­tics com­plex­ol­ogy