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

• Wel­come to the Group Pat­tern Lan­guage Project | Group Works

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

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

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

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

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

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

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

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

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

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

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

• Curat­ing the kraken « Pub­lic Historian

  ‘This is why “curate” is still a word to con­jure by in our cul­ture.  It still promises trans­for­ma­tive power.’
  
  muse­ol­ogy prag­mat­ics nam­ing engineering-​​of-​​philosophy

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

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